Methods and compositions for determining risk of treatment toxicity

ABSTRACT

Methods are provided for determining whether a patient treated with an anti-proliferative agent is susceptible to toxicity. In practicing the subject methods, an expression profile for the transcriptional response to a therapy is obtained from the patient and compared to a reference profile to determine whether the patient is susceptible to toxicity. In addition, reagents and kits thereof that find use in practicing the subject methods are provided.

[0001] Many anti-proliferative agents used to treat cancer; infections, etc. also have the potential to damage normal cells. Generally dosage levels are selected to preferentially affect the target, e.g. tumor cells, but some patients are particularly susceptible to toxicity, and can suffer undesirable side effects from such treatment.

[0002] For example, ionizing radiation (IR) is used to treat about 60% of cancer patients, by depositing energy that injures or destroys cells in the area being treated. Radiation injury to cells is nonspecific, with complex effects on DNA. The efficacy of therapy depends on cellular injury to cancer cells being greater than to normal cells. Radiotherapy may be used to treat every type of cancer. Some types of radiation therapy involve photons, such as X-rays or gamma rays. Another technique for delivering radiation to cancer cells is internal radiotherapy, which places radioactive implants directly in a tumor or body cavity so that the radiation dose is concentrated in a small area.

[0003] Radiotherapy may be used in combination with additional agents. Radiosensitizers make the tumor cells more likely to be damaged, and radioprotectors protect normal tissues from the effects of radiation. Hyperthermia is also being studied for its effectiveness in sensitizing tissue to radiation.

[0004] Although most patients tolerate treatment, up to 10% of patients suffer from toxicity that can lead to significant morbidity. Non-genetic risk factors for radiation toxicity include concurrent treatment with radiosensitizing drugs and anatomical variations such as congenital malformations, post-surgical adhesions, fat content, and tissue oxygenation. Toxicity is also associated with diabetes and autoimmune diseases such as lupus. However, these causes cannot account for the vast majority of adverse radiation reactions.

[0005] In a small fraction of cases, radiation sensitivity can be attributed to known genetic mutations. Diseases of IR sensitivity include ataxia telangiectasia (AT), AT-like disorder, Nijmegan Breakage Syndrome, and radiosensitivity with severe combined immunodeficiency, but these autosomal recessive diseases are rare. Heterozygosity for mutations in ATM, the gene mutated in AT, may occur in 1% of individuals and has been reported to confer moderate sensitivity to IR in tissue culture. However, relatively few adverse radiation reactions are associated with ATM mutations.

[0006] Several attempts have been made to correlate radiation toxicity with cellular responses to IR ex vivo. Survival of cultured skin fibroblasts after IR correlated with acute radiation toxicity in some studies but not others (see Johansen et al. (1996) Radiother Oncol 40:101-9; Russell et al. (1998) Int J Radiat Biol 73:661-70; Peacock et al. (2000) Radiother Oncol 55:173-8. In another study, lymphocytes from cancer patients with radiation toxicity showed less IR-induced apoptosis than lymphocytes from control patients (Crompton et al. (1999) Int J Radiat Oncol Biol Phys 45:707-714). Peripheral blood lymphocytes from breast cancer patients with severe skin reactions showed an abnormal increase in chromosome aberrations when the cells were exposed to IR (Barber et al. (2000) Radiother Oncol 55:179-86). In these latter two studies, correlations between radiation toxicity and the ex vivo assay suggested the presence of an underlying genetic defect in some radiation sensitive patients. However, there was a large overlap between radiation sensitive patients and controls in these assays, limiting their clinical usefulness. Thus, assays to predict radiation toxicity have yielded mixed results, and the vast majority of adverse reactions remain unexplained (Brock et al. (2000) Radiother Oncol 55:93-94).

[0007] To date, there is no effective way known to the inventors to predict whether or not a patient will be susceptible to toxicity following radiation therapy. A diagnostic protocol which could provide information as to whether a patient is or is not susceptible to toxicity would be desirable for a number of reasons, including avoidance of delays in alternative treatments, elimination of exposure to adverse effects and reduction of unnecessary expense. As such, there is interest in the development of a protocol that can accurately predict whether or not a patient is susceptible to toxicity from radiation therapy.

[0008] Relevant Literature

[0009] A method of analyzing the significance of changes observed in expression patterns in microarrays may be found in International Application WO 01/84139; and Tusher et al. (2001) Proc. Natl. Acad. Sci. USA 98:5116-5121. A method for analysis of shrunken centroids is described by Tibshirani et al. (2002) Proc. Natl. Acad. Sci. USA 99:6567-6572.

SUMMARY OF THE INVENTION

[0010] Methods are provided for predicting whether an individual subjected to anti-proliferative therapy, particularly therapy that results in DNA damage, e.g. radiation therapy will be susceptible to toxicity resulting from the therapy. The ability to predict susceptibility to toxicity allows optimization of treatment, and determination of whether on whether to proceed with a specific therapy, and how to optimize dose, choice of treatment, and the like. In another embodiment, methods are provided for determining whether an individual is susceptible to toxicity.

[0011] In practicing the methods, an expression profile is obtained from the subject cells in the absence and presence of the therapy, e.g. UV radiation, ionizing radiation, presence of a chemotherapeutic agent, etc. The expression profile is used to determine the difference between the exposed and non-exposed cells, and is compared to a reference profile. Reagents and kits thereof that find use in practicing the subject methods are provided.

[0012] In another embodiment of the invention, methods are provided for statistical analysis of data, such as expression profiles in response to a stimulus, e.g. treatment with drug, exposure to radiation, exposure to specific antigenic stimulus, and the like; post-translational responses, basal expression levels; etc. to determine whether a pattern of expression or response will be predictive of a phenotype of interest. The statistical analyses usually utilize a heterogeneity-associated transformation, and nearest shrunken centroids analysis to provide a set of predictive genes.

BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. FIGS. 1A-1F. Effect of heterogeneity-associated transformation (HAT) on gene expression data. The left panels show changes in gene expression after DNA damage, x(i), for gene i. The dashed line marks {overscore (x)}_(c)(i), the average x(i) among the controls. The right panels show data after HAT, which was more effective in separating the radiation sensitive patients from controls. The upper panels show a hypothetical gene with transcriptional responses that were blunted in some patients and enhanced in others. The middle and lower panels show actual data for two predictive genes, cyclin B and 8-oxo-dGTPase. Patient samples were arranged by predicted probability for radiation toxicity (see FIG. 3).

[0014] FIGS. 2A-2B. Effect of heterogeneity-associated transformation (HAT) on predictive power. The nearest shrunken centroid (NSC) classifier was applied to 1491 IR-responsive genes and 2114 UV-responsive genes identified by SAM. In the NSC method, the threshold parameter determines the number of genes used for prediction (shown above the bar graphs). The upper and lower panels show the number of errors with and without HAT, respectively. White bars indicate the number of false negatives, and black bars indicate the number of false positives.

[0015] FIGS. 3A-3B. Predicting radiation toxicity from transcriptional responses to IR and UV. The plots show predictions for 15 subjects with no cancer (NoCa), 15 patients with skin cancer (SkCa), 13 control cancer patients without toxicity from radiation therapy (RadC), and 14 radiation sensitive cancer patients (RadS). HAT/NSC identified 24 predictive genes represented by 25 probe sets. The IR and UV responses were used to compute the probability of toxicity for each subject. The dotted lines indicate probability of 0.5, the prospectively defined cutoff for predicting radiation toxicity. The upper panel shows probabilities for radiation toxicity calculated from the full 48-sample training set. To avoid selection bias (see Ambroise and McLachlan (2002) P.N.A.S. 99:6562-6566), the 9 NoCa subjects were excluded from the training set because these subjects were used to identify the IR and UV-responsive genes. The lower panel shows probabilities calculated from 14-fold cross-validation as described in the text. The 9 NoCa subjects were excluded from the training sets, but included for cross-validation.

[0016]FIG. 4. Hierarchical clustering of genes that predict radiation toxicity. Data are shown for the 52 top-ranked predictive genes identified by HAT/NSC. The dendrogram above the heat map shows clustering of the 57 subjects. Shaded boxes under the dendrogram indicate the classes of subjects. The dendrogram to the left of the heat map shows clustering of the 52 genes represented by 55 probe sets. The colored boxes to the right of the heat map indicate biological function of the genes. An asterisk next to the gene description indicates UV-response data. All other data are IR-response data. Accession number, symbol, and rank in our prediction protocol are listed for each gene. Three predictive genes are listed twice, since two different probe sets (specified in parentheses) for the same gene were found to be predictive. In each case, probe sets for the same gene were closely clustered. Because centered Pearson correlation was used for clustering, genes with changes in expression that varied in the same way across samples were clustered together, independently of average changes in expression. For example, CALM1 and BASP1, two genes at the top of the heat map, were clustered together even though CALM1 was generally repressed and BASP1 was generally induced. To provide a scale for the IR-response data, the upper right panel shows the distribution of average IR responses for all 12,625 probe sets in samples from 15 subjects without cancer. The distribution of UV responses was similar.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The subject invention provides a method of determining whether a patient is susceptible to toxicity resulting from anti-proliferative therapy, where the method includes (a) obtaining a transcriptional response profile for a sample from said subject in the absence or presence of said therapy; and (b) comparing said obtained profile to a reference expression profile to determine whether said subject is susceptible to said toxicity. In certain embodiments, the expression profile is for at least about 10, usually at least about 25, and may be at least 50, at least about 100, or more of said genes listed in Table 3. In certain embodiments, the expression profile is determined using a microarray. In other embodiments the expression profile is determined by quantitative PCR or other quantitative methods for measuring mRNA.

[0018] The subject invention also provides a reference expression profile for a response phenotype that is one of: (a) susceptible to toxicity; or (b) non-susceptible to toxicity; wherein said expression profile is recorded on a computer readable medium.

[0019] For quantitative PCR analysis, the subject invention provides a collection of gene specific primers, said collection comprising: gene specific primers specific for at least about 10, usually at least about 20 of the genes of Table 3, where in certain embodiments said collection comprises at least 50 gene specific primers, at least 100, or more. The subject invention also provides an array of probe nucleic acids immobilized on a solid support, said array comprising: a plurality of probe nucleic acid compositions, wherein each probe nucleic acid composition is specific for a gene whose expression profile is indicative of toxicity susceptibility phenotype, wherein at least 10 of said probe nucleic acid compositions correspond to genes listed in Table 3, where in certain embodiments said array further comprises at least one control nucleic acid composition.

[0020] The subject invention also provides a kit for use in determining the susceptibility phenotype of a source of a nucleic acid sample, said kit comprising: at least one of: (a) an array as described above; or (b) a collection of gene specific primers as described above. The kit may further comprise a software package for data analysis of expression profiles.

[0021] Before the subject invention is described further, it is to be understood that the invention is not limited to the particular embodiments of the invention described below, as variations of the particular embodiments may be made and still fall within the scope of the appended claims. It is also to be understood that the terminology employed is for the purpose of describing particular embodiments, and is not intended to be limiting. Instead, the scope of the present invention will be established by the appended claims. In this specification and the appended claims, the singular forms “a,” “an” and “the” include plural reference unless the context clearly dictates otherwise.

[0022] Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range, and any other stated or intervening value in that stated range, is encompassed within the invention. The upper a nd lower limits of these smaller ranges may independently be included in the smaller ranges, and are also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

[0023] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood to one of ordinary skill in the art to which this invention belongs. Although any methods, devices and materials similar or equivalent to those described herein can be used in the practice or testing of the invention, the preferred methods, devices and materials are now described.

[0024] All publications mentioned herein are incorporated herein by reference for the purpose of describing and disclosing the subject components of the invention that are described in the publications, which components might be used in connection with the presently described invention.

[0025] As summarized above, the subject invention is directed to methods of determining whether a subject is susceptible to unacceptable toxicity in response to therapeutic procedures, as well as reagents and kits for use in practicing the subject methods. The methods may also determine whether a particular cancer cell is susceptible to killing by a therapy of interest, where the differential between the target cell, e.g. a cancer cell, and the normal cell, is useful in making a determination of suitable treatment.

[0026] Methods are also provided for optimizing therapy, by determining the susceptibility of a patient to toxicity induced by one or more therapies, and based on that information, selecting the appropriate therapy, dose, treatment modality, e.g. angle and screening of radiation, etc. which optimizes the differential between delivery of an anti-proliferative treatment to the undesirable target cells, while minimizing undesirable toxicity. In one embodiment of the invention, the patient sample is exposed to two or more candidate therapies or combinations of therapies, e.g. exposure to various chemotherapeutic agents. Optionally, both a normal cell sample and a tumor cell sample are tested, in order to determine the differential effect of the treatment on normal and tumor cells. The treatment is optimized by selection for a treatment that avoids treatment that has a high probability of causing undesirable toxicity, while providing for effective anti-proliferative activity.

[0027] In further describing the invention, the subject methods are described first, followed by a review of the reagents and kits for use in practicing the subject methods.

Anti-Proliferative Agents and Treatments

[0028] Anti-proliferative therapy is used therapeutically to eliminate tumor cells and other undesirable cells in a host, and includes the use of therapies such as delivery of ionizing radiation, and administration of chemotherapeutic agents. Chemotherapeutic agents of particular interest induce DNA damage, and more particularly agents of interest induce double stranded breaks in DNA, for example the topoisomerase inhibitors anthracyclines, including the compounds daunorubicin, adriamycin (doxorubicin), epirubicin, idarubicin, anamycin, MEN 10755, and the like. Other topoisomerase inhibitors include the podophyllotoxin analogues etoposide and teniposide, and the anthracenediones, mitoxantrone and amsacrine.

[0029] In one aspect of the invention, the anti-proliferative agent interferes with microtubule assembly, e.g. the family of vinca alkaloids. Examples of vinca alkaloids include vinblastine, vincristine; vinorelbine (NAVELBINE); vindesine; vindoline; vincamine; etc.

[0030] In another embodiment of the invention, the anti-proliferative agent is a DNA-damaging agent, such as nucleotide analogs, alkylating agents, etc. Alkylating agents include nitrogen mustards, e.g. mechlorethamine, cyclophosphamide, melphalan (L-sarcolysin), etc.; and nitrosoureas, e.g. carmustine (BCNU), lomustine (CCNU), semustine (methyl-CCNU), streptozocin, chlorozotocin, etc.

[0031] Nucleotide analogs include pyrimidines, e.g. cytarabine (CYTOSAR-U), cytosine arabinoside, fluorouracil (5-FU), floxuridine (FUdR), etc.; purines, e.g. thioguanine (6-thioguanine), mercaptopurine (6-MP), pentostatin, fluorouracil (5-FU) etc.; and folic acid analogs, e.g. methotrexate, 10-propargyl-5,8-dideazafolate (PDDF, CB3717), 5,8-dideazatetrahydrofolic acid (DDATHF), leucovorin, etc.

[0032] Other chemotherapeutic agents of interest include metal complexes, e.g. cisplatin (cis-DDP), carboplatin, oxaliplatin, etc.; ureas, e.g. hydroxyurea; and hydrazines, e.g. N-methylhydrazine.

Toxicity

[0033] The use of anti-proliferative agents and treatments in therapy, e.g. in cancer therapy, depends on a differential between the effect on undesirable cancer cells and normal cells. Certain patients are less tolerant of treatment, and suffer unacceptable toxicity in normal tissues. It will be understood by those of skill in the art that some level of damage may occur in all subjects. It will also be understood that the toxic effects may be found on various tissues, i.e. skin, central nervous system, gut, etc. depending on the specific angle and dose of therapeutic radiation, compound that is delivered, etc. Criteria for grading toxic effects are known in the art, and are reproduced herein for convenience. The methods of the present invention are useful in differentiating between patients susceptible to unacceptable toxicity, i.e. having a grade of 2, 3, 4 or 5 in any tissue; and patients susceptible to acceptable toxicity of only grade 0 or 1.

[0034] The following tables provide conventional criteria for grading radiation toxicity. Other toxicities associated with other agents are known in the relevant clinical arts, and will be readily obtained by one of skill in the art. Toxicity may occur within less than about 90 days following exposure, herein termed early toxicity, or may occur after greater than about 90 days, herein termed late toxicity. TABLE 1 Early Toxicity [0] [1] [2] [3] [4] Skin No change Follicular, faint or Tender or bright Confluent, moist Ulceration, over baseline dull erythema/epilation/dry erythema, patchy moist desquamatiom other than hemorrhage, necrosis desquamation/decreased desquamation/ skin folds, pitting edema sweating moderate edema Mucous Membrane No change Injection/may experience Patchy mucositis which Confluent fibrinous Ulceration, hemorrhage over baseline mild pain not requiring may produce an mucositis/may include or necrosis analgesic inflammatory severe pain requiring serosanguinitis narcotic discharge/may experience moderate pain requiring analgesia Eye No change Mild conjunctivitis with or Moderate conjunctivitis Severe keratitis with Loss of vision without scleral injection/ with or without keratitis corneal ulceration/ (unilateral or bilateral) increased tearing requiring steroids &/or objective decrease in antibiotics/dry eye visual acuity or in visual requiring artificial tears/ fields/acute glaucoma/ iritis with photophobia panopthalmitis Ear No change Mild external otitis with Moderate external otitis Severe external otitis with Deafness over baseline erythema, pruritis, requiring topical dischange or moist secondary to dry medication/serious desquamation/ desquamation not otitis medius/ symptomatic requiring medication. hypoacusis on testing hypoacusis/tinnitus, not Audiogram unchanged only drug related from baseline Salivary Gland No change Mild mouth dryness/ Moderate to complete Acute salivary gland over baseline slightly thickened saliva/ dryness/thick, sticky necrosis may have slightly altered saliva/markedly altered taste such as metallic taste taste Pharynx & No change Mild dysphagia or Moderate dysphagia or Severe dysphagia or Complete obstruction, Esophagus over baseline odynophagia/may odynophagia/may odynophagia with ulceration, perforation, require topical anesthetic require narcotic dehydration or weight fistula or non-narcotic analgesics/may require loss (>15% from pretreatment analgesics/may require puree or liquid diet baseline) soft diet requiring N-G feeding tube, I.V. fluids or hyperalimentation Larynx No change Mild or intermittent Persistent hoarseness Whispered speech, throat Marked dyspnea, over baseline hoarseness/cough not but able to vocalize/ pain or referred ear pain stridor or hemoptysis requiring antitussive/ referred ear pain, sore requiring narcotic/ with tracheostomy or erythema of mucosa throat, patchy fibrinous confluent fibrinous intubation necessary exudate or mild exudate, marked arytenoid edema not arytenoid edema requiring narcotic/ cough requiring antitussive Upper G.I. No change Anorexia with <=5% Anorexia with <=15% Anorexia with >15% Ileus, subacute or acute weight loss from weight loss from weight loss from obstruction, pretreatment baseline/ pretreatment pretreatment baseline or performation, GI nausea not requiring baseline/nausea &/or requiring N-G tube or bleeding requiring antiemetics/abdominal vomiting requiring parenteral support. transfusion/abdominal discomfort not requiring antiemetics/abdominal Nausea &/or vomiting pain requiring tube parasympatholytic drugs pain requiring requiring tube or decompression or or analgesics analgesics parenteral bowel diversion support/abdominal pain, severe despite medication/hematemesis or melena/abdominal distention (flat plate radiograph demonstrates distended bowel loops Lower G.I. No change Increased frequency or Diarrhea requiring Diarrhea requiring Acute or subacute Including change in quality of bowel parasympatholytic drugs parenteral support/ obstruction, fistula or Pelvis habits not requiring (e.g., Lomotil)/mucous severe mucous or blood perforation; GI bleeding medication/rectal discharge not discharge necessitating requiring transfusion; discomfort not requiring necessitating sanitary sanitary pags/abdominal abdominal pain or analgesics pads/rectal or distention (flat plate tenesmus requiring abdominal pain radiograph demonstrates tube decompression or requiring analgesics distended bowel loops) bowel diversion Lung No change Mild symptoms of dry Persistent cough Severe cough Severe respiratory cough or dyspnea on requiring narcotic, unresponsive to narcotic insufficiency/ exertion antitussive agents/ antitussive agent or continuous oxygen or dyspnea with minimal dyspnea at rest/clinical assisted ventilation effort but not at rest or radiologic evidence of acute pneumonitis/ intermittent oxygen or steroids may be required Genitourinary No change Frequency of urination or Frequency of urination Frequency with urgency Hematuria requiring nocturia twice or nocturia which is less and nocturia hourly or transfusion/acute pretreatment habit/ frequent than every more frequently/dysuria, bladder obstruction not dysuria, urgency not hour. Dysuria, urgency, pelvis pain or bladder secondary to clot requiring medication bladder spasm requiring spasm requiring regular, passage, ulceration or local anesthetic (e.g., frequent narcotic/gross necrosis Pyridium) hematuria with/without clot passage Heart No change Asymptomatic but Symptomatic with EKG Congestive heart failure, Congestive heart over baseline objective evidence of changes and radiologic angina pectoris, failure, angina pectoris, EKG changes or findings of congestive pericardial disease pericardial disease, pericardial abnormalities heart failure or responding to therapy arrhythmias not without evidence of other pericardial disease/no responsive to non- heart disease specific treatment surgical measures required Cns No change Fully functional status Neurologic findings Neurologic findings Serious neurologic (i.e., able to work) with present sufficient to requiring hospitalization impairment which minor neurologic findings, require home case/ for initial management includes paralysis, no medication needed nursing assistance may coma or seizures >3 per be required/ week despite medications including medication/hospitalization steroids/anti-seizure required agents may be required Hematologic >=4.0 3.0-<4.0 2.0-<3.0 1.0-<2.0 <1.0 Wbc (X 1000) Platelets (X >100 75-<100 50-<=75 25-<50 <25 or spontaneous 1000) bleeding Neutrophils >=1.9 1.5-<1.9 1.0-<1.5 0.5-<=1.0 <=0.5 or sepsis Hemoglobin >11 11-9.5 <9.5-7.5 <7.5-5.0 — (Gm %) Hematocrit >=32 28-<32 <=28 Packed cell transfusion — (%) required

[0035] TABLE 2 Late Toxicity Organ Tissue 0 Grade 1 Grade 2 Grade 3 Grade 4 Skin None Slight atrophy Patch atrophy; Marked atrophy; Gross Ulceration Pigmentation change Moderate telangiectasia; telangiectasia Some hair loss Total hair loss Subcutaneous None Slight induration (fibrosia) Moderate fibrosis but Severe induration and Necrosis Tissue and loss of subcutaneous asymptomatic Slight loss of subcutaneous fat field contracture <10% tissue Field contracture linear reduction >10% linear measurement Mucous None Slight atrophy and Moderate atrophy and Marked atrophy with Ulceration Membrane dryness telangiectasia Little complete dryness Severe mucous telangiectasia Salivary None Slight dryness of mouth Moderate dryness of Complete dryness of Fibrosis Glands Good response on mouth Poor response mouth No response on stimulation on stimulation stimulation Spinal Cord None Mild L'Hermitte's Severe L'Hermitte's Objective neurological Mono, para syndrome syndrome findings at or below cord quadraplegia level treated Brain None Mild headache Slight Moderate headache Severe headaches Seizures or paralysis lethargy Great lethargy Severe CNS dysfunction Coma (partial loss of power or dyskinesia) Eye None Asymptomatic cataract Symptomatic cataract Severe keratitis Severe Panopthalmitis/ Minor corneal ulceration Moderate corneal retinopathy or Blindness or keratitis ulceration Minor detachment Severe retinopathy or glaucoma glaucoma Larynx None Hoarseness Slight Moderate arytenoid Severe edema Severe Necrosis arytenoid edema edema Chondritis chondritis Lung None Asymptomatic or mild Moderate symptomatic Severe symptomatic Severe respiratory symptoms (dry cough) fibrosis or pneumonitis fibrosis or pneumonitis insufficiency/ Slight radiographic (severe cough) Low Dense radiographic Continuous O2/ appearances grade fever Patchy changes Assisted ventilation radiographic appearances Heart None Asymptomatic or mild Moderate angina on Severe angina Pericardial Tamponade/Severe symptoms Transient T effort Mild pericarditis effusion Constrictive heart failure/Severe wave inversion & ST Normal heart size pericarditis Moderate constrictive pericarditis changes Sinus Persistent abnormal T heart failure Cardiac tachycardia >110 (at rest) wave and ST changes enlargement EKG Low ORS abnormalities Esophagus None Mild fibrosis Slight Unable to take solid Severe fibrosis Able to Necrosis/Perforation difficulty in swallowing food normally swallow only liquids May Fistula solids No pain on Swallowing semi-solid have pain on swallowing swallowing food Dilatation may be Dilation required indicated Small/Large None Mild diarrhea Mild Moderate diarrhea and Obstruction or bleeding Necrosis/ Intestine cramping Bowel colic Bowel movement requiring surgery PerforationFistula movement 5 times daily >5 times daily Slight rectal discharge or Excessive rectal mucus bleeding or intermittent bleeding Liver None Mild lassitude Nausea, Moderate symptoms Disabling hepatitic Necrosis/Hepatic dyspepsia Slightly Some abnormal liver insufficiency Liver coma or abnormal liver function function tests Serum function tests grossly encephalopathy albumin normal abnormal Low albumin Edema or ascites Kidney None Transient albuminuria No Persistent moderate Severe albuminuria Malignant hypertension Mild albuminuria (2+)Mild Severe hypertension hypertension Uremic impairment of renal hypertension No related Persistent anemia coma/Urea >100% function Urea 25-35 mg % anemia Moderate (<10 g %) Severe renal Creatinine 1.5-2.0 mg % impairment of renal failure Urea >60 mg % Creatinine clearance function Urea > 36-60 Creatinine >4.0 mg % >75% mg % Creatinine Creatinine clearance clearance (50-74%) <50% Bladder None Slight epithelial atrophy Moderate frequency Severe frequency and Necrosis/Contracted Minor telangiectasia Generalized dysuria Severe bladder (capacity <100 cc) (microscopic hematuria) telangiectasia generalized Severe Intermittent macroscopic telangiectasia (often with hemorrhagic cystitis hematuria petechiae) Frequent hematuria Reduction in bladder capacity (<150 cc) Bone None Asymptomatic No growth Moderate pain or Severe pain or Necrosis/ retardation Reduced tenderness Growth tenderness Complete Spontaneous fracture bone density retardation Irregular arrest of bone growth bone sclerosis Dense bone sclerosis Joint None Mild joint stiffness Slight Moderate stiffness Severe joint stiffness Pain Necrosis/Complete limitation of movement Intermittent or moderate with severe limitation of fixation joint pain Moderate movement limitation of movement

Methods of Determining Susceptibility

[0036] The subject invention provides methods of predicting whether a patient or subject exposed to anti-proliferative therapy, particularly therapy resulting in double stranded DNA damage, e.g. ionizing radiation, including X-rays, gamma radiation, etc.; treatment with topoisomerase inhibitors as described above, and the like; will be susceptible to toxicity. In practicing the subject methods, a subject or patient sample, e.g., cells or collections thereof, e.g., tissues, is assayed to determine whether the host from which the assayed sample was obtained is susceptible to toxicity. Cells of interest particularly include dividing cells, e.g. leukocytes, fibroblasts, epithelial cells, etc. Cell samples are collected by any convenient method, as known in the art. Additionally, tumor cells may be collected and tested to determine the relative effectiveness of a therapy in causing differential death between normal and diseased cells.

[0037] To test for radiation-induced toxicity, the cell sample is exposed to radiation, including at least ionizing radiation, and preferably one cell sample is exposed to ionizing radiation and a second cell sample is exposed to ultraviolet radiation. A suitable dose of ionizing radiation may range from at least about 2 Gy to not more than about 10 Gy, usually about 5 Gy. The sample may be collected from at least about 2 and not more than about 24 hours following ionizing radiation, usually around about 4 hours. A suitable dose of ultraviolet radiation may range from at least about 5 J/m² to not more than about 50 J/m², usually about 10 J/m². The sample may be collected from at least about 4 and not more than about 72 hours following ultraviolet radiation, usually around about 4 hours. The radiation exposed cell sample is assayed to obtain an expression profile for a set of genes, typically including at least about 10 top ranked genes set forth in Table 3, usually including at least about 25 top ranked genes, and may include at least about 50 top ranked genes; 100 top-ranked genes, or more, up to the complete set of predictive genes.

[0038] To test for toxicity resulting from exposure to chemotherapeutic agents, the cell sample may be exposed to radiation, as described above, or may be exposed to the therapeutic agent of interest, or to an agent having a similar profile of activity. Typically a cell sample will be compared to a control sample that has not been exposed to the therapy. The dose and time period for obtaining samples following exposure will vary with the specific agent that is selected. As is known in the art, a titration of dose may be used to determine the appropriate range for testing. Generally, samples from the cells will be obtained after at least about 4 hours and not more than about 5 days following exposure.

[0039] The term expression profile is used broadly to include a genomic expression profile, e.g., an expression profile of mRNAs, or a proteomic expression profile, e.g., an expression profile of one or more different proteins. Profiles may be generated by any convenient means for determining differential gene expression between two samples, e.g. quantitative hybridization of mRNA, labeled mRNA, amplified mRNA, cRNA, etc., quantitative PCR, ELISA for protein quantitation, and the like.

[0040] Genes/proteins of interest are genes/proteins that are found to be predictive of susceptibility to toxicity include, but are not limited to, the genes/proteins provided in Table 3, below TABLE 3 IR or UV Rank Accession Symbol Name response 1 M25753 HUMCYCB Cyclin B UV 2 AI436567 ATP5D ATP synthase, H+ transporting, mitochondrial F1 IR complex, delta subunit 3 X54942 CKS2 CDC28 protein kinase 2 UV 4 AB011126 FBP17 formin-binding protein 17 IR 5 U14971 RPS9 ribosomal protein S9 IR 6 AL022318 MDS019 phorbolin-like protein MDS019 IR 7 L08096 TNFSF7 tumor necrosis factor (ligand) superfamily, member 7 IR 8 AL080113 RNA helicase IR 9 AI126004 SAS10 disrupter of silencing 10 IR 10 Z23090 HSPB1 heat shock 27 kD protein 1 IR 11 D21090 RAD23B RAD23 homolog B IR 12 U35451 CBX1 chromobox homolog 1 (HP1 beta) IR 13 AA890010 IR 14 M65028 HNRPAB heterogeneous nuclear ribonucleoprotein A/B IR 15 D26600 PSMB4 proteasome (prosome, macropain) subunit, beta type, 4 IR 16 AF072810 BAZ1B bromodomain adjacent to zinc finger domain, 1B IR 17 U49869 ubiquitin IR 18 D16581 NUDT1 nudix (nucleoside diphosphate linked moiety X)-type IR motif 1 19 AA121509 LOC51690 U6 snRNA-associated Sm-like protein LSm7 IR 20 X81625 ETF1 eukaryotic translation termination factor 1 IR 21 Z48501 PABPC1 poly(A)-binding protein, cytoplasmic 1 IR 22 AA121509 LOC51690 U6 snRNA-associated Sm-like protein LSm7 IR 23 U12022 CALM1 calmodulin UV 24 U52682 IRF4 interferon regulatory factor 4 IR 25 J03592 SLC25A6 solute carrier family 25 (mitochondrial carrier; adenine IR nucleotide translocator), member 6 26 J03161 SRF serum response factor (c-fos serum response IR element-binding transcription factor) 27 Z11692 EEF2 eukaryotic translation elongation factor 2 IR 28 X83218 ATP5O ATP synthase, H+ transporting, mitochondrial F1 IR complex, O subunit (oligomycin sensitivity conferring protein) 29 X51688 CCNA2 cyclin A2 UV 30 U11861 G10 maternal G10 transcript IR 31 D44466 PSMD1 proteasome (prosome, macropain) 26S subunit, non- IR ATPase, 1 32 AB019392 M9 muscle specific gene IR 33 AI991040 DRAP1 DR1-associated protein 1 (negative cofactor 2 alpha) IR 34 X70944 SFPQ splicing factor proline/glutamine rich (polypyrimidine UV tract-binding protein-associated) 35 M25753 Cyclin B1 UV 36 X15414 AKR1B1 aldo-keto reductase family 1, member B1 (aldose IR reductase) 37 U12779 MAPKAPK2 mitogen-activated protein kinase-activated protein IR kinase 2 38 Z49254 MRPL23 mitochondrial ribosomal protein L23 IR 39 J02683 SLC25A5 solute carrier family 25 (mitochondrial carrier; adenine UV nucleotide translocator), member 5 40 S87759 PPM1A protein phosphatase 1A (formerly 2C), magnesium- IR dependent, alpha isoform 41 D32050 AARS alanyl-tRNA synthetase UV 42 X06617 RPS11 ribosomal protein S11 IR 43 AF023676 TM7SF2 transmembrane 7 superfamily member 2 IR 44 AB002368 KIAA0370 KIAA0370 protein IR 45 AB029038 KIAA1115 KIAA1115 protein IR 46 D45248 PSME2 proteasome (prosome, macropain) activator subunit 2 IR (PA28 beta) 47 D13641 KIAA0016 translocase of outer mitochondrial membrane 20 IR (yeast) homolog 48 M58378 IR 49 Y18418 RUVBL1 RuvB (E coli homolog)-like 1 UV 50 L20298 CBFB core-binding factor, beta subunit IR 51 L24804 P23 unactive progesterone receptor, 23 kD UV 52 AF039656 BASP1 brain abundant, membrane attached signal protein 1 UV 53 AL022721 PPARD peroxisome proliferative activated receptor, delta IR 54 U48734 ACTN4 actinin, alpha 4 IR 55 Z49148 RPL29 ribosomal protein L29 IR 56 U68063 SFRS10 splicing factor, arginine/serine-rich (transformer UV homolog) 10 57 AJ005259 EDF1 endothelial differentiation-related factor 1 IR 58 U05340 CDC20 CDC20 (cell division cycle 20 homolog) UV 59 M72709 SFRS1 splicing factor, arginine/serine-rich 1 (splicing factor 2, UV alternate splicing factor) 60 U15932 DUSP5 dual specificity phosphatase 5 UV 61 M61764 TUBG1 tubulin, gamma 1 UV 62 AI857469 TCEB2 transcription elongation factor B (SIII), polypeptide 2 IR (18 kD, elongin B) 63 AL022318 MDS019 phorbolin-like protein MDS019 UV 64 AB011114 KIAA0542 KIAA0542 gene product IR 65 X71874 IR 66 L07956 GBE1 glucan (1,4-alpha-), branching enzyme 1 (glycogen IR branching enzyme 67 AF053356 IR 68 L31584 EBI 1 G protein-coupled receptor IR 69 X78992 ZFP36L2 zinc finger protein 36, C3H type-like 2 IR 70 M81757 RPS19 ribosomal protein S19 IR 71 AL031670 IR 72 W07033 GMFG glia maturation factor, gamma IR 73 Z98046 IR 74 U47101 NIFU nitrogen fixation cluster-like IR 75 L11566 RPL18 ribosomal protein L18 IR 76 U75686 polyadenylate binding protein UV 77 M83664 HLA-DPB1 major histocompatibility complex, class II, DP beta 1 UV 78 AL050021 IR 79 M93425 PTPN12 protein tyrosine phosphatase, non-receptor type 12 IR 80 U94905 DGKZ diacylglycerol kinase, zeta (104 kD) UV 81 Y08614 XPO1 exportin 1 (CRM1, yeast, homolog) IR 82 AI540957 QP-C low molecular mass ubiquinone-binding protein IR (9.5 kD) 83 Z26876 RPL38 ribosomal protein L38 IR 84 U28386 KPNA2 karyopherin alpha 2 (RAG cohort 1, importin alpha 1) IR 85 X65550 MKI67 antigen identified by monoclonal antibody Ki-67 UV 86 S72008 CDC10 CDC10 (cell division cycle 10 homolog) IR 87 U03398 TNFSF9 tumor necrosis factor (ligand) superfamily, member 9 IR 88 AF049910 TACC1 transforming, acidic coiled-coil containing protein 1 IR 89 D42043 KIAA0084 KIAA0084 protein IR 90 AB002313 PLXNB2 plexin B2 UV 91 X97074 AP2S1 adaptor-related protein complex 2, sigma 1 subunit IR 92 AB002323 DNCH1 dynein, cytoplasmic, heavy polypeptide 1 UV 93 AF047185 NDUFA2 NADH dehydrogenase (ubiquinone) 1 alpha IR subcomplex, 2 (8 kD, B8) 94 AI819948 MEL mel transforming oncogene (derived from cell line UV NK14)-RAB8 homolog 95 U14970 RPS5 ribosomal protein S5 IR 96 AI375913 TOP2A topoisomerase (DNA) II alpha (170 kD) IR 97 AI541050 NDUFB8 NADH dehydrogenase (ubiquinone) 1 beta IR subcomplex, 8 (19 kD, ASHI) 98 D86979 KIAA0226 KIAA0226 gene product IR 99 Z36714 CCNF cyclin F IR 100 M30938 XRCC5 X-ray repair complementing defective repair (double- UV strand-break rejoining; Ku autoantigen) 101 J03191 PFN1 profilin 1 UV 102 X65923 FAU ribosomal protein S30 IR 103 AF035555 HADH2 hydroxyacyl-Coenzyme A dehydrogenase, type II IR 104 X72889 SMARCA2 SWI/SNF related, matrix associated, actin dependent IR regulator of chromatin, subfamily a, member 2 105 L22473 BAX BCL2-associated X protein UV 106 U09813 ATP5G3 ATP synthase, H+ transporting, mitochondrial F0 IR complex, subunit c (subunit 9) isoform 3 107 Y00371 hsc70 71 kd heat shock cognate protein IR 108 U94855 EIF3S5 eukaryotic translation initiation factor 3, subunit 5 IR (epsilon, 47 kD) 109 AA808961 PSMB9 proteasome (prosome, macropain) subunit, beta type, IR 9 (large multifunctional protease 2) 110 AF053356 UV 111 AF005392 UV 112 L01124 RPS13 ribosomal protein S13 IR 113 X00457 HLA-DPA1 major histocompatibility complex, class II, DP alpha 1 UV 114 AI800499 AIM1 absent in melanoma 1 IR 115 Y08110 SORL1 sortilin-related receptor, L(DLR class) A repeats- UV containing 116 U12472 GSTP1 glutathione 5-transferase pi IR 117 X78992 ZFP36L2 zinc finger protein 36, C3H type-like 2 UV 118 X91257 SARS seryl-tRNA synthetase IR 119 M81757 RPS19 ribosomal protein S19 UV 120 AF037448 NSAP1 NS1-associated protein 1 IR 121 AL022394 UV 122 U67156 MAP3K5 mitogen-activated protein kinase kinase kinase 5 IR 123 AF087135 ATP5H ATP synthase, H+ transporting, mitochondrial F0 IR complex, subunit d 124 N24355 POLR2L polymerase (RNA) II (DNA directed) polypeptide L IR (7.6 kD) 125 D78134 CIRBP cold inducible RNA-binding protein IR 126 X81625 ETF1 eukaryotic translation termination factor 1 UV 127 X13710 GPX1 glutathione peroxidase 1 IR 128 U18321 DAP3 death associated protein 3 IR 129 AF072810 BAZ1B bromodomain adjacent to zinc finger domain, 1B UV 130 X82240 TCL1A T-cell leukemia/lymphoma 1A IR 131 D26598 PSMB3 proteasome (prosome, macropain) subunit, beta type, 3 IR 132 X97548 TRIM28 tripartite motif-containing 28 UV 133 D49738 CKAP1 cytoskeleton-associated protein 1 IR 134 D87078 PUM2 pumilio homolog 2 IR 135 U49278 UBE2V1 ubiquitin-conjugating enzyme E2 variant 1 UV 136 U18300 DDB2 damage-specific DNA binding protein 2 (48 kD) IR 137 X70394 ZNF146 zinc finger protein 146 IR 138 AF041259 ZNF217 zinc finger protein 217 IR 139 M94314 RPL24 ribosomal protein L24 IR 140 U09510 GARS glycyl-tRNA synthetase UV 141 AF042384 BC-2 putative breast adenocarcinoma marker (32 kD) IR 142 HG1800-HT1823 IR 143 U96915 SAP18 sin3-associated polypeptide, 18 kD IR 144 M13934 ribosomal protein S14 IR 145 Z11697 CD83 CD83 antigen (activated B lymphocytes, IR immunoglobulin superfamily) 146 U19599 BAX BCL2-associated X protein IR 147 AA527880 IR 148 U48734 ACTN4 actinin, alpha 4 UV 149 U14972 RPS10 ribosomal protein S10 IR 150 D00760 PSMA2 proteasome (prosome, macropain) subunit, alpha IR type, 2 151 M86667 NAP1L1 nucleosome assembly protein 1-like 1 UV 152 AF057557 TOSO regulator of Fas-induced apoptosis IR 153 U59309 FH fumarate hydratase UV 154 AL049701 KIAA0471 KIAA0471 gene product UV 155 AB029014 KIAA1091 KIAA1091 protein UV 156 D23661 RPL37 ribosomal protein L37 IR 157 U03106 CDKN1A cyclin-dependent kinase inhibitor 1A (p21, Cip1) UV 158 AC004770 UV 159 AF037643 RPL12 ribosomal protein L12 IR 160 U07424 FARSL phenylalanine-tRNA synthetase-like UV 161 AA806768 Homo sapiens phorbolin I protein (PBI) mRNA, UV complete cds 162 L49380 ZNF162 zinc finger protein 162 UV 163 AL050366 OGT O-linked N-acetylglucosamine (GlcNAc) transferase IR (UDP-N-acetylglucosamine: polypeptide-N- acetylglucosaminyl transferase) 164 L12723 HSPA4 heat shock 70 kD protein 4 IR 165 M13932 RPS17 ribosomal protein S17 IR 166 U51004 HINT histidine triad nucleotide-binding protein IR 167 M64716 RPS25 ribosomal protein S25 IR 168 Z11697 CD83 CD83 antigen (activated B lymphocytes, UV immunoglobulin superfamily) 169 N98670 IR 170 U14966 RPL5 ribosomal protein L5 IR 171 D13643 DHCR24 24-dehydrocholesterol reductase UV 172 D21262 NOLC1 nucleolar and coiled-body phosphprotein 1 IR 173 AC005943 UV 174 AF044671 GABARAP GABA(A) receptor-associated protein IR 175 U54559 EIF3S3 eukaryotic translation initiation factor 3, subunit 3 IR (gamma, 40 kD) 176 J04130 SCYA4 small inducible cytokine A4 (homologous to mouse IR Mip-1b) 177 U19599 BAX BCL2-associated X protein UV 178 X57206 ITPKB inositol 1,4,5-trisphosphate 3-kinase B UV 179 D87446 KIAA0257 KIAA0257 protein UV 180 T58471 UQCR ubiquinol-cytochrome c reductase (6.4 kD) subunit IR 181 U02570 ARHGAP1 Rho GTPase activating protein 1 UV 182 X51688 CCNA2 cyclin A2 UV 183 D31885 ARL6IP ADP-ribosylation factor-like 6 interacting protein UV 184 AI541336 NDUFS5 NADH dehydrogenase (ubiquinone) Fe-S protein 5 IR (15 kD) (NADH-coenzyme Q reductase) 185 V00567 B2M beta-2-microglobulin IR 186 M86737 SSRP1 structure specific recognition protein 1 UV 187 D80005 C9orf10 C9orf10 protein UV 188 AF017789 TAF2S TATA box binding protein (TBP)-associated factor, IR RNA polymerase II, S, 150 kD 189 AB014458 USP1 ubiquitin specific protease 1 UV 190 X63469 GTF2E2 general transcription factor IIE, polypeptide 2 (beta IR subunit, 34 kD) 191 M55914 ENO1 enolase 1, (alpha) IR 192 Y00451 ALAS1 aminolevulinate, delta-, synthase 1 UV 193 AF046001 ZNF207 zinc finger protein 207 UV dolichyl-diphosphooligosaccharide-protein 194 D29643 DDOST glycosyltransferase IR 195 U29344 FASN fatty acid synthase UV 196 L13848 DDX9 DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 9 UV (RNA helicase A, nuclear DNA helicase II; leukophysin) 197 J00314 TUBB tubulin, beta polypeptide IR 198 X71874 UV 199 D90070 PMAIP1 phorbol-12-myristate-13-acetate-induced protein 1 IR 200 X64330 ACLY ATP citrate lyase UV 201 M94362 LMNB2 lamin B2 IR 202 M23114 ATP2A2 ATPase, Ca++ transporting, cardiac muscle, slow UV twitch 2 203 J03040 SPARC secreted protein, acidic, cysteine-rich (osteonectin) IR 204 X64229 DEK DEK oncogene (DNA binding) IR 205 J03826 FDXR ferredoxin reductase UV 206 U51698 DED apoptosis antagonizing transcription factor UV 207 Z37166 BAT1 HLA-B associated transcript 1 IR 208 X62744 HLA-DMA major histocompatibility complex, class II, DM alpha IR 209 U28686 RBM3 RNA binding motif protein 3 UV 210 D00860 PRPS1 phosphoribosyl pyrophosphate synthetase 1 UV 211 L76200 GUK1 guanylate kinase 1 IR 212 AB011118 KIAA0546 KIAA0546 protein IR 213 L08895 MEF2C MADS box transcription enhancer factor 2, IR polypeptide C (myocyte enhancer factor 2C) 214 D38551 RAD21 RAD21 homolog IR 215 M32578 HLA-DRB1 major histocompatibility complex, class II, DR beta 1 UV 216 X66079 SPIB Spi-B transcription factor (Spi-1/PU.1 related) IR 217 U03398 TNFSF9 tumor necrosis factor (ligand) superfamily, member 9 UV 218 Y13936 PPM1G protein phosphatase 1G (formerly 2C), magnesium- IR dependent, gamma isoform 219 X15940 RPL31 ribosomal protein L31 IR 220 J04031 MTHFD1 methylenetetrahydrofolate dehydrogenase (NADP+ UV dependent), methenyltetrahydrofolate cyclohydrolase, formyltetrahydrofolate synthetase 221 AI032612 SNRPF small nuclear ribonucleoprotein polypeptide F IR 222 AJ245416 LSM2 U6 snRNA-associated Sm-like protein IR 223 L25931 LBR lamin B receptor UV 224 J05614 IR 225 AL050265 TARDBP TAR DNA binding protein UV 226 X04366 CAPN1 calpain 1, (mu/l) large subunit UV 227 AL050161 IR 228 D42084 METAP1 methionyl aminopeptidase 1 IR 229 U90878 PDLIM1 PDZ and LIM domain 1 (elfin) IR 230 AL080109 KIAA0618 KIAA0618 gene product IR 231 U94319 PSIP2 PC4 and SFRS1 interacting protein 2 IR 232 L15189 HSPA9B heat shock 70 kD protein 9B (mortalin-2) UV 233 X80199 MLN51 MLN51 protein IR 234 AL050060 DKFZP566H073 DKFZP566H073 protein UV 235 X59543 RRM1 ribonucleotide reductase M1 polypeptide UV 236 AB019987 SMC4L1 SMC4 (structural maintenance of chromosomes 4)- UV like 1 237 J04977 XRCC5 X-ray repair complementing defective repair (double- UV strand-break rejoining; Ku autoantigen, 80 kD) 238 Y07969 SSP29 acidic protein rich in leucines UV 239 U37690 POLR2L polymerase (RNA) II (DNA directed) polypeptide L IR (7.6 kD) 240 AB018328 ALTE Ac-like transposable element IR 241 AI540925 COX6A1 cytochrome c oxidase subunit VIa polypeptide 1 IR 242 HG1515-HT1515 Btf3b Transcription Factor Btf3b IR 243 U87947 EMP3 epithelial membrane protein 3 UV 244 AB028990 KIAA1067 KIAA1067 protein IR 245 X55954 RPL23 ribosomal protein L23 IR 246 X02994 ADA adenosine deaminase UV 247 AB029038 KIAA1115 KIAA1115 protein UV 248 L29254 IR 249 U05040 Homo sapiens far upstream element (FUSE) binding UV protein 1 (FUBP1), mRNA 250 AF007140 ILF3 interleukin enhancer binding factor 3, 90 kD UV 251 X59303 VARS2 valyl-tRNA synthetase 2 UV 252 AI345944 NDUFB1 NADH dehydrogenase (ubiquinone) 1 beta IR subcomplex, 1 (7 kD, MNLL) 253 U21689 GSTP1 glutathione S-transferase pi IR 254 Z24459 IR 255 U45878 BIRC3 baculoviral IAP repeat-containing 3 UV 256 AF081280 NPM3 nucleophosmin/nucleoplasmin 3 UV 257 Z25535 NUP153 nucleoporin 153 kD IR 258 D26579 ADAM8 a disintegrin and metalloproteinase domain 8 IR 259 AF063308 DEEPEST mitotic spindle coiled-coil related protein UV 260 S57212 MEF2C MADS box transcription enhancer factor 2, IR polypeptide C (myocyte enhancer factor 2C) 261 Y00971 PRPS2 phosphoribosyl pyrophosphate synthetase 2 UV 262 AF067656 ZWINT ZW10 interactor UV 263 M91196 ICSBP1 interferon consensus sequence binding protein 1 IR 264 AI033692 BCRP1 Breakpoint cluster region protein, uterine leiomyoma, UV 1; barrier to autointegration factor 265 AL022326 SYNGR1 synaptogyrin 1 IR 266 AF032885 FOXO1A forkhead box O1A (rhabdomyosarcoma) UV 267 U03911 MSH2 mutS homolog 2 (colon cancer, nonpolyposis type 1) UV 268 AL021154 IR 269 AB011116 KIAA0544 KIAA0544 protein IR 270 X17644 GSPT1 G1 to S phase transition 1 UV 271 AI565760 GABARAPL2 GABA(A) receptor-associated protein-like 2 IR 272 D87735 RPL14 ribosomal protein L14 IR 273 U52112 IRAK1 interleukin-1 receptor-associated kinase 1 UV 274 X04803 ubiquitin IR 275 AI525834 NPC2 Niemann-Pick disease, type C2 gene IR 276 M14333 FYN FYN oncogene related to SRC, FGR, YES UV 277 Z97054 UREB1 upstream regulatory element binding protein 1 UV 278 AB014609 KIAA0709 endocytic receptor (macrophage mannose receptor UV family) 279 AI653621 TXN thioredoxin UV 280 U24266 ALDH4A1 aldehyde dehydrogenase 4 family, member A1 UV 281 M37583 H2AFZ H2A histone family, member Z UV 282 J03805 PPP2CB protein phosphatase 2 (formerly 2A), catalytic subunit, UV beta isoform 283 U51127 IRF5 interferon regulatory factor 5 UV 284 M22806 P4HB prolyl 4-hydroxylase beta-subunit and disulfide UV isomerase 285 D11086 IL2RG interleukin 2 receptor, gamma (severe combined UV immunodeficiency) 286 AF000982 DDX3 DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 3 UV 287 U86602 EBNA1BP2 EBNA1-binding protein 2 UV 288 AF000231 RAB11A RAB11A, member RAS oncogene family UV 289 L23959 TFDP1 transcription factor Dp-1 UV 290 AB020713 KIAA0906 KIAA0906 protein UV 291 X59871 TCF7 transcription factor 7 (T-cell specific, HMG-box) UV 292 AA310786 Homo sapiens cDNA: FLJ23602 fis, clone LNG15735 IR 293 U15085 HLA-DMB major histocompatibility complex, class II, DM beta IR 294 D80001 KIAA0179 KIAA0179 protein IR 295 HG4074-HT4344 Rad2 Rad2 UV 296 AA648295 CBX3 chromobox homolog 3 (HP1 gamma) UV 297 Y13936 PPM1G protein phosphatase IG (formerly 2C), magnesium- UV dependent, gamma isoform 298 D49489 P5 protein disulfide isomerase-related protein UV 299 AJ012590 H6PD hexose-6-phosphate dehydrogenase (glucose 1- IR dehydrogenase) 300 D16431 HDGF hepatoma-derived growth factor (high-mobility group IR protein 1-like) 301 AA527880 IR 302 AI525665 COX8 cytochrome c oxidase subunit VIII IR 303 U19765 ZNF9 zinc finger protein 9 (a cellular retroviral nucleic acid UV binding protein) 304 M74491 ARF3 ADP-ribosylation factor 3 UV 305 AF039397 UV 306 X67951 PRDX1 peroxiredoxin 1 IR 307 AB005047 SH3BP5 SH3-domain binding protein 5 (BTK-associated) IR 308 S75463 TUFM Tu translation elongation factor, mitochondrial UV 309 M63904 GNA15 guanine nucleotide binding protein (G protein), alpha UV 15 (Gq class) 310 D42084 METAP1 methionyl aminopeptidase 1 UV 311 W28979 FLJ20452 hypothetical protein FLJ20452 IR 312 M59465 TNFAIP3 tumor necrosis factor, alpha-induced protein 3 IR 313 M26004 CR2 complement component, receptor 2 IR 314 X04106 CAPNS1 calpain, small subunit 1 IR 315 Z14000 RING1 ring finger protein 1 UV 316 AF044671 GABARAP GABA(A) receptor-associated protein UV 317 D13627 CCT8 chaperonin containing TCP1, subunit 8 (theta) UV 318 D21853 KIAA0111 KIAA0111 gene product UV 319 HG662-HT662 Small Rna-Associated Protein IR 320 AI087268 SNRPC small nuclear ribonucleoprotein polypeptide C IR 321 D80000 SMC1L1 SMC1 (structural maintenance of chromosomes 1)- UV like 1 322 L31584 EBI 1 G protein-coupled receptor UV 323 M33336 PRKAR1A protein kinase, cAMP-dependent, regulatory, type I, alpha (tissue specific extinguisher 1) UV 324 D14812 KIAA0026 MORF-related gene X UV 325 D11139 TIMP1 tissue inhibitor of metalloproteinase 1 (erythroid UV potentiating activity, collagenase inhibitor) 326 M65028 HNRPAB heterogeneous nuclear ribonucleoprotein A/B UV 327 AB023154 KIAA0937 KIAA0937 protein UV 328 AA149486 COX17 COX17 homolog, cytochrome c oxidase assembly IR protein 329 Y00371 hsc70 71 kd heat shock cognate protein UV 330 X95808 ZNF261 zinc finger protein 261 IR 331 M64595 RAC2 ras-related C3 botulinum toxin substrate 2 (rho family, IR small GTP binding protein Rac2) 332 D50405 HDAC1 histone deacetylase 1 UV 333 X95384 UK114 translational inhibitor protein p14.5 UV 334 M93311 MT3 metallothionein 3 (growth inhibitory factor IR (neurotrophic)) 335 M13792 ADA adenosine deaminase UV 336 D90070 PMAIP1 phorbol-12-myristate-13-acetate-induced protein 1 UV 337 AF047436 ATP5J2 ATP synthase, H+ transporting, mitochondrial F0 UV complex, subunit f, isoform 2 338 U24152 PAK1 p21/Cdc42/Rac1-activated kinase 1 (yeast Ste20- UV related) 339 U46692 cystatin B IR

[0041] In certain embodiments, any one or more of the genes/proteins in the prepared expression profile are from Table 3, above, where the expression profile may include expression data for 5, 10, 20, 25, 50, 100 or more of, including all of, the genes/proteins listed in Table 3, above.

[0042] In certain embodiments, the expression profile obtained is a genomic or nucleic acid expression profile, where the amount or level of one or more nucleic acids in the sample is determined. In these embodiments, the sample that is assayed to generate the expression profile employed in the diagnostic methods is one that is a nucleic acid sample. The nucleic acid sample includes a plurality or population of distinct nucleic acids that includes the expression information of the phenotype determinative genes of interest of the cell or tissue being diagnosed. The nucleic acid may include RNA or DNA nucleic acids, e.g., mRNA, cRNA, cDNA etc., so long as the sample retains the expression information of the host cell or tissue from which it is obtained.

[0043] The sample may be prepared in a number of different ways, as is known in the art, e.g., by mRNA isolation from a cell, where the isolated mRNA is used as is, amplified, employed to prepare cDNA, cRNA, etc., as is known in the differential expression art. The sample is typically prepared from a cell or tissue harvested from a subject to be diagnosed, e.g., via blood drawing, biopsy of tissue, using standard protocols, where cell types or tissues from which such nucleic acids may be generated include any tissue in which the expression pattern of the to be determined phenotype exists. Cells may be cultured prior to analysis.

[0044] The expression profile may be generated from the initial nucleic acid sample using any convenient protocol. While a variety of different manners of generating expression profiles are known, such as those employed in the field of differential gene expression analysis, one representative and convenient type of protocol for generating expression profiles is array based gene expression profile generation protocols. Such applications are hybridization assays in which a nucleic acid that displays “probe” nucleic acids for each of the genes to be assayed/profiled in the profile to be generated is employed. In these assays, a sample of target nucleic acids is first prepared from the initial nucleic acid sample being assayed, where preparation may include labeling of the target nucleic acids with a label, e.g., a member of signal producing system. Following target nucleic acid sample preparation, the sample is contacted with the array under hybridization conditions, whereby complexes are formed between target nucleic acids that are complementary to probe sequences attached to the array surface. The presence of hybridized complexes is then detected, either qualitatively or quantitatively.

[0045] Specific hybridization technology which may be practiced to generate the expression profiles employed in the subject methods includes the technology described in U.S. Pat. Nos. 5,143,854; 5,288,644; 5,324,633; 5,432,049; 5,470,710; 5,492,806; 5,503,980; 5,510,270; 5,525,464; 5,547,839; 5,580,732; 5,661,028; 5,800,992; the disclosures of which are herein incorporated by reference; as well as WO 95/21265; WO 96/31622; WO 97/10365; WO 97/27317; EP 373 203; and EP 785 280. In these methods, an array of “probe” nucleic acids that includes a probe for each of the phenotype determinative genes whose expression is being assayed is contacted with target nucleic acids as described above. Contact is carried out under hybridization conditions, e.g., stringent hybridization conditions as described above, and unbound nucleic acid is then removed. The resultant pattern of hybridized nucleic acid provides information regarding expression for each of the genes that have been probed, where the expression information is in terms of whether or not the gene is expressed and, typically, at what level, where the expression data, i.e., expression profile, may be both qualitative and quantitative.

[0046] Alternatively, non-array based methods for quantitating the levels of one or more nucleic acids in a sample may be employed, including quantitative PCR, and the like.

[0047] Where the expression profile is a protein expression profile, any convenient protein quantitation protocol may be employed, where the levels of one or more proteins in the assayed sample are determined. Representative methods include, but are not limited to; proteomic arrays, flow cytometry, standard immunoassays, etc.

[0048] Following obtainment of the expression profile from the sample being assayed, the expression profile is compared with a reference or control profile to make a diagnosis regarding the radiation toxicity susceptibility phenotype of the cell or tissue from which the sample was obtained/derived. Typically a comparison is made with a set of cells from the same source, which has not been exposed to radiation. Additionally, a reference or control profile may be a profile that is obtained from a cell/tissue known to have the susceptible phenotype, and therefore may be a positive reference or control profile. In addition, a reference/control profile may be from a cell/tissue known to not have the susceptibility phenotype, and therefore be a negative reference/control profile.

[0049] In certain embodiments, the obtained expression profile is compared to a single reference/control profile to obtain information regarding the phenotype of the cell/tissue being assayed. In yet other embodiments, the obtained expression profile is compared to two or more different reference/control profiles to obtain more in depth information regarding the phenotype of the assayed cell/tissue. For example, the obtained expression profile may be compared to a positive and negative reference profile to obtain confirmed information regarding whether the cell/tissue has the phenotype of interest.

[0050] The difference values, i.e. the difference in expression in the presence and absence of radiation may be performed using any convenient methodology, where a variety of methodologies are known to those of skill in the array art, e.g., by comparing digital images of the expression profiles, by comparing databases of expression data, etc. Patents describing ways of comparing expression profiles include, but are not limited to, U.S. Pat. Nos. 6,308,170 and 6,228,575, the disclosures of which are herein incorporated by reference. Methods of comparing expression profiles are also described above.

[0051] A statistical analysis step is then performed to obtain the weighted contribution of the set of predictive genes. Nearest shrunken centroids analysis, is applied as described in Tibshirani et al. (2002) P.N.A.S. 99:6567-6572 to compute the centroid for each class, then compute the average squared distance between a given expression profile and each centroid, normalized by the within-class standard deviation.

[0052] To perform a shrunken centroids analysis, let x_(ik) be the expression for genes i=1, 2, . . . p and samples j=1, 2, . . . n. Classes are 1, 2, . . . K, and C_(k) is indices of the n_(k) samples in class k. The ith component of the centroid for class k is {overscore (x)}_(ik)=ΣjεC_(k)x_(ij)n_(k)/n_(k) the mean expression value in class k for gene i; the ith component of the overall centroid is {overscore (x)}_(i)=Σ_(j=1x) _(ii) _(/n) ^(n). In words, one shrinks the class centroids toward the overall centroids after standardizing by the within-class standard deviation for each gene. This standardization has the effect of giving higher weight to genes whose expression is stable within samples of the same class. $\begin{matrix} {{d_{ik} = \frac{{\overset{\_}{x}}_{ik} - {\overset{\_}{x}}_{i}}{m_{k} \cdot \left( {s_{i} + s_{o}} \right)}},} & \lbrack 1\rbrack \end{matrix}$

[0053] where s_(i) is the pooled within-class standard deviation for gene i: $\begin{matrix} {s_{i}^{2} = {\frac{1}{n - K}{\sum\limits_{k}{\sum\limits_{j \in C_{k}}\left( {x_{ij} - {\overset{\_}{x}}_{ik}} \right)^{2}}}}} & \lbrack 2\rbrack \end{matrix}$

[0054] and mk={square root}{square root over (1/n_(k)+1/n)} makes m_(k)·s_(i) equal to the estimated standard error of the numerator in d_(ik). In the denominator, the value so is a positive constant (with the same value for all genes), included to guard against the possibility of large d_(ik) values arising by chance from genes with low expression levels. s₀ is set to be equal to the median value of the s_(i) over the set of genes.

[0055] Thus d_(ik) is a t statistic for gene i, comparing class k to the overall centroid. Eq. 1 can be rewritten as

{overscore (x)} _(ik) ={overscore (x)} _(i) +m _(k)(s _(i) +s _(o))d _(ik)  [3]

[0056] This method shrinks each d_(ik) toward zero, giving d_(ik) and yielding shrunken centroids or prototypes

{overscore (x)}′ _(ik) ={overscore (x)} _(i) +m _(k)(s _(i) +s ₀)d′ _(ik)  [4]

[0057] The shrinkage is called soft thresholding: each d_(ik) is reduced by an amount Δ in absolute value and is set to zero if its absolute value is less than zero. Algebraically, soft thresholding is defined by

d′ _(ik)=sign)(d _(ik))(|d _(ik)|−Δ)₊  [5]

[0058] where + means positive part (t₊=t if t>0 and zero otherwise). Because many of the {overscore (x)}_(ik) values will be noisy and close to the overall mean {overscore (x)}_(i), soft thresholding produces more reliable estimates of the true means. This method has the desirable property that many of the components (genes) are eliminated from the class prediction as the shrinkage parameter Δ is increased. Specifically, if for a gene i, d_(ik) is shrunken to zero for all classes k, then the centroid for gene i is {overscore (x)}_(i), the same for all classes. Thus gene i does not contribute to the nearest-centroid computation.

[0059] Depending on the type and nature of the reference/control profile(s) to which the obtained expression profile is compared, the above comparison step yields information as to whether a patient is susceptible to toxicity after exposure to antiproliferative therapy. As such, the above comparison step can yield a positive/negative determination of a susceptible phenotype of an assayed cell/tissue.

[0060] The prediction of susceptibility is probabilistically defined, where the cut-off for predicted susceptibility may be empirically derived, for example as shown in FIG. 3. In one embodiment of the invention, a probability of about 0.4 may be used to distinguish between susceptible and non-susceptible patients, more usually a probability of about 0.5, and may utilize a probability of about 0.6 or higher. A “high” probability may be at least about 0.75, at least about 0.7, at least about 0.6, or at least about 0.5. A “low” probability may be not more than about 0.25, not more than 0.3, or not more than 0.4. In many embodiments, the above-obtained information about the cell/tissue being assayed is employed to predict whether a host, subject or patient is treated with a therapy of interest, e.g. treatment with ionizing radiation, exposure to a chemotherapeutic agent etc., and to optimize the dose therein.

Databases of Expression Profiles

[0061] Also provided are databases of expression profiles of phenotype determinative genes. Such databases will typically comprise expression profiles of various cells/tissues having susceptible phenotypes, negative expression profiles, etc., where such profiles are further described below.

[0062] The expression profiles and databases thereof may be provided in a variety of media to facilitate their use. “Media” refers to a manufacture that contains the expression profile information of the present invention. The databases of the present invention can be recorded on computer readable media, e.g. any medium that can be read and accessed directly by a computer. Such media include, but are not limited to: magnetic storage media, such as floppy discs, hard disc storage medium, and magnetic tape; optical storage media such as CD-ROM; electrical storage media such as RAM and ROM; and hybrids of these categories such as magnetic/optical storage media. One of skill in the art can readily appreciate how any of the presently known computer readable mediums can be used to create a manufacture comprising a recording of the present database information. “Recorded” refers to a process for storing information on computer readable medium, using any such methods as known in the art. Any convenient data storage structure may be chosen, based on the means used to access the stored information. A variety of data processor programs and formats can be used for storage, e.g. word processing text file, database format, etc.

[0063] As used herein, “a computer-based system” refers to the hardware means, software means, and data storage means used to analyze the information of the present invention. The minimum hardware of the computer-based systems of the present invention comprises a central processing unit (CPU), input means, output means, and data storage means. A skilled artisan can readily appreciate that any one of the currently available computer-based system are suitable for use in the present invention. The data storage means may comprise any manufacture comprising a recording of the present information as described above, or a memory access means that can access such a manufacture.

[0064] A variety of structural formats for the input and output means can be used to input and output the information in the computer-based systems of the present invention. Such presentation provides a skilled artisan with a ranking of similarities and identifies the degree of similarity contained in the test expression profile.

Reagents and Kits

[0065] Also provided are reagents and kits thereof for practicing one or more of the above-described methods. The subject reagents and kits thereof may vary greatly. Reagents of interest include reagents specifically designed for use in production of the above described expression profiles of phenotype determinative genes.

[0066] One type of such reagent is an array of probe nucleic acids in which the phenotype determinative genes of interest are represented. A variety of different array formats are known in the art, with a wide variety of different probe structures, substrate compositions and attachment technologies. Representative array structures of interest include those described in U.S. Pat. Nos. 5,143,854; 5,288,644; 5,324,633; 5,432,049; 5,470,710; 5,492,806; 5,503,980; 5,510,270; 5,525,464; 5,547,839; 5,580,732; 5,661,028; 5,800,992; the disclosures of which are herein incorporated by reference; as well as WO 95/21265; WO 96/31622; WO 97/10365; WO 97/27317; EP 373 203; and EP 785 280. In certain embodiments, the number of genes that are from Table 3 that is represented on the array is at least 10, usually at least 25, and may be at least 50, 100, up to including all of the genes listed in Table 3, preferably utilizing the top ranked set of genes. The subject arrays may include only those genes that are listed in Table 3, or they may include additional genes that are not listed in Table 3. Where the subject arrays include probes for such additional genes, in certain embodiments the number % of additional genes that are represented does not exceed about 50%, usually does not exceed about 25%. In many embodiments where additional “non-Table 3” genes are included, a great majority of genes in the collection are phenotype determinative genes, where by great majority is meant at least about 75%, usually at least about 80% and sometimes at least about 85, 90, 95% or higher, including embodiments where 100% of the genes in the collection are predictive genes.

[0067] Another type of reagent that is specifically tailored for generating expression profiles of phenotype determinative genes is a collection of gene specific primers that is designed to selectively amplify such genes, for use in quantitative PCR and other quantitation methods. Gene specific primers and methods for using the same are described in U.S. Pat. No. 5,994,076, the disclosure of which is herein incorporated by reference. Of particular interest are collections of gene specific primers that have primers for at least 10 of the genes listed in Table 3, above, often a plurality of these genes, e.g., at least 25, and may be 50, 100 or more to include all of the genes listed in Table 3. The subject gene specific primer collections may include only those genes that are listed in Table 3, or they may include primers for additional genes that are not listed in Table 3. Where the subject gene specific primer collections include primers for such additional genes, in certain embodiments the number % of additional genes that are represented does not exceed about 50%, usually does not exceed about 25%. In many embodiments where additional “non-Table 3” genes are included, a great majority of genes in the collection are phenotype determinative genes, where by great majority is meant at least about 75%, usually at least about 80% and sometimes at least about 85, 90, 95% or higher, including embodiments where 100% of the genes in the collection are predictive genes.

[0068] The kits of the subject invention may include the above described arrays and/or gene specific primer collections. The kits may further include a software package for statistical analysis of one or more phenotypes, and may include a reference database for calculating the probability of susceptibility. The kit may include reagents employed in the various methods, such as primers for generating target nucleic acids, dNTPs and/or rNTPs, which may be either premixed or separate, one or more uniquely labeled dNTPs and/or rNTPs, such as biotinylated or Cy3 or Cy5 tagged dNTPs, gold or silver particles with different scattering spectra, or other post synthesis labeling reagent, such as chemically active derivatives of fluorescent dyes, enzymes, such as reverse transcriptases, DNA polymerases, RNA polymerases, and the like, various buffer mediums, e.g. hybridization and washing buffers, prefabricated probe arrays, labeled probe purification reagents and components, like spin columns, etc., signal generation and detection reagents, e.g. streptavidin-alkaline phosphatase conjugate, chemifluorescent or chemiluminescent substrate, and the like.

[0069] In addition to the above components, the subject kits will further include instructions for practicing the subject methods. These instructions may be present in the subject kits in a variety of forms, one or more of which may be present in the kit. One form in which these instructions may be present is as printed information on a suitable medium or substrate, e.g., a piece or pieces of paper on which the information is printed, in the packaging of the kit, in a package insert, etc. Yet another means would be a computer readable medium, e.g., diskette, CD, etc., on which the information has been recorded. Yet another means that may be present is a website address which may be used via the internet to access the information at a removed site. Any convenient means may be present in the kits.

Method of Analyzing Genes for Predictive Value

[0070] In another aspect of the invention, methods are provided for identifying genes and proteins that are predictive of a phenotype of interest. Such analytical methods provide a set of molecules whose pattern of expression yields information about a phenotype of interest. The molecules may be transcriptional responses, expression of a protein, post-translational protein modification, e.g. cleavage, phosphorylation and dephosporylation, glycosylation, etc.

[0071] The pattern of expression may be basal levels of expression in a target cell type, e.g. expression of a gene in a cancer cell, differential expression of a gene in a normal v. a cancer cell, expression of a gene during a specific developmental stage, basal phosphorylation of a protein in a cell, and the like. The pattern of expression may also be in response to a treatment of interest, e.g. exposure to radiation, exposure to a therapeutic agent, exposure to cytokines, response of cells in a mixed lymphocyte reaction, and the like. The shrunken centroid analysis described above may be used to determine an expression profile for any phenotype of interest.

[0072] The phenotype of interest may be susceptibility to toxicity, response to a therapeutic regimen or agent, development of autoimmune disease, development of graft rejection, development of graft v. host disease, distinction of heterogeneity in an early stage of cancer, e.g. prediction of probable course of disease, and the like.

[0073] To obtain the set of predictive genes, initially cohorts are gathered for the phenotype of interest, e.g. patients suffering from a disease of interest, responders and non-responders to a treatment of interest, and the like. One or more cohorts are gathered for the phenotype of interest, and one or more for a control, preferably a matched control group, according to methods known in the art.

[0074] An expression profile for the trait to be examined is made. Convenient methods for examining large groups of genes include hybridization to microarrays, as discussed above and in the examples. Alternatively, proteomics arrays may be used to determine protein profiles, antibody array can be used to detect the presence of epitopes of interest in a sample, various methods known in the art for quantitative hybridization of a nucleic acid may be used, and the like. As discussed above, the basal expression level may be taken, or a response to a particular stimulus. In many cases it is desirable to determine a difference in expression between a control and a test sample. The expression may be normalized a control, to expression of a housekeeping gene or genes, etc., as known in the art.

[0075] Many phenotypes of interest are actually the result of different underlying genotypes, where a heterogeneous response over a patient population can make analysis difficult. To address the problem of heterogeneity, the following heterogeneity-associated transformation (HAT) is performed, using the following equation: $\begin{matrix} {{x^{\prime}(i)} = \left\lbrack {{x(i)} - {{\overset{\_}{x}}_{c}(i)}} \right\rbrack^{2}} & \lbrack 6\rbrack \end{matrix}$

[0076] where x(i) is the change in expression for gene i, and {overscore (x)}_(c)(i) is the average change in expression for gene i among the control samples. HAT generates equivalent values for changes in gene expression that are blunted in some cases and enhanced in others, and hence can capture heterogeneous abnormalities among the radiation sensitive patients. Genes with divergent transcriptional responses might be overlooked by comparing the average response of controls to the average response, but are successfully identified after transforming the data.

[0077] After transforming the data, nearest shrunken centroid analysis is performed, as described above and in Tibshirani et al. (2002), supra. The centroid of gene expression for a class of samples is defined as a multi-component vector, in which each component is the expression of a gene averaged over the samples. Samples are then classified by proximity to the nearest centroid. In order to verify the prediction, it is desirable to test profiles against an independent set of samples, or with cross-validation.

[0078] The probability of a specific outcome is then calculated. The cut-off for a particular diagnosis will be determined empirically, based on the specific set of data, and may be modeled to include the weighted probability for rare events.

[0079] The above-described analytical methods may be embodied as a program of instructions executable by computer to perform the different aspects of the invention. Any of the techniques described above may be performed by means of software components loaded into a computer or other information appliance or digital device. When so enabled, the computer, appliance or device may then perform the above-described techniques to assist the analysis of sets of values associated with a plurality of genes in the manner described above, or for comparing such associated values. The software component may be loaded from a fixed media or accessed through a communication medium such as the internet or other type of computer network. The above features are embodied in one or more computer programs may be performed by one or more computers running such programs.

[0080] The following examples are offered by way of illustration and not by way of limitation.

EXPERIMENTAL

[0081] Toxicity from radiation therapy is a grave problem for cancer patients, and methods are needed for predicting its occurrence. Microarrays were used to analyze abnormal transcriptional responses to DNA damage in cultured lymphocytes. A transformation of the data was devised to account for the possibility that toxicity can arise from defects in different pathways. The risk of toxicity was then computed for each patient using nearest shrunken centroids, a method that identifies predictive genes. Transcriptional responses in 24 genes predicted radiation toxicity in 9 of 14 patients with no false positives among 43 controls. Some patients had defective responses to ionizing radiation, while others had defective responses to both ultraviolet and ionizing radiation. This approach has the potential to predict toxicity from ionizing radiation and other anticancer agents, enabling physicians to design a safe treatment plan for each patient.

[0082] Materials and Methods

[0083] Patient cell lines. Subjects were enrolled with informed consent between 1997 and 2002 in accordance with Stanford regulations for human subjects research. Radiation toxicity was graded according to the RTOG Acute and Late Radiation Morbidity Scoring Criteria. Radiation therapy patients donated peripheral blood samples at least 2 months following completion of treatment and resolution of any toxicity. Lymphoblastoid cell lines were established by immortalization of peripheral blood B-lymphocytes with Epstein-Barr virus from the B95-8 monkey cell line. Cells were grown in RPMI 1640 (Gibco) with 15% heat inactivated fetal bovine serum, 1% penicillin/streptomycin, and 2 mM glutamine and stored in liquid nitrogen.

[0084] Treatment of cells with UV and IR. Lymphoblastoid cells were subjected to mock, UV, and IR treatment. For UV treatment, 5×10⁷ cells were suspended in PBS at 6×10⁵ cells/ml to ensure uniform exposure to UV. Cells subjected to mock and IR treatment were also suspended in PBS during this period to ensure similar treatment. For UV treatment, cells were exposed for 15 sec to a germicidal lamp at a fluence of 0.67 J/m²/sec to deliver a 10 J/m² dose, seeded at 3×10⁵ cells/ml in fresh media, and harvested for RNA 24 hrs later. For IR treatment, 4×10⁷ cells were exposed to 5 Gy IR 20 hrs after the PBS wash and harvested for RNA 4 hrs later.

[0085] Microarray hybridization. Total RNA was labeled with biotin and hybridized to a U95A_v2 GeneChip® microarray, according to manufacturer's protocols (Affymetrix, Santa Clara, Calif.). The expression level for each gene was calculated by Affymetrix GeneChip Microarray Analysis Suite software version 4.0. To account for differences in hybridization between different chips, data from hybridizations were scaled to the average of all data sets, as described by Tusher et al. (2001) Proc. Natl. Acad. Sci. USA; 98:5116-5121.

[0086] Analysis of microarray data. The data was in the form of change in gene expression, computed for each individual as the difference in expression before and after exposure to UV or IR. Analyses were based on changes in gene expression, because this was less sensitive to variation among different individuals than the basal or induced levels of expression. Thus, we used the paired data option in Significance Analysis of Microarrays (SAM), which ranks genes by change in expression relative to the standard deviation in multiple samples. IR-responsive and UV-responsive genes were identified using data from 9 normal individuals.” The false discovery rate (FDR) is the percentage of genes falsely called significant when the change in gene expression for each individual is randomly chosen to be left unaltered or multiplied by −1. Responsive genes were obtained by choosing a threshold corresponding to an FDR of 10%.

[0087] The nearest shrunken centroid (NSC) classifier was applied to the radiation toxicity and control classes (Tibshirani et al. (2002) Proc. Natl. Acad. Sci. USA 99:6567-6572). The centroid for a class of samples was defined as a multi-component vector, in which each component was the expression of a predictive gene averaged over the samples in that class. NSC shrinks the class centroids towards the overall centroid after normalizing by the within-class standard deviation for each gene. The probability for radiation toxicity associated with an expression profile was computed from its distances to the radiation toxicity and control centroids.

[0088] The accuracy of a supervised classifier such as NSC may appear to be high when applied to the training samples, i.e., the samples used to define the centroids. However, this is not statistically valid. The number of genes is much greater than the number of samples in microarray experiments, providing many opportunities to find genes with expression patterns that correlate with the class of interest. Thus, supervised classifiers are susceptible to overfitting, and their accuracy must be tested by cross-validation on samples not used for training Ambroise and McLachlan (2002) Proc Natl Acad Sci USA; 99:6562-6566.

[0089] We subjected NSC to 14-fold cross-validation by dividing the samples into 14 subsets. Each subset contained one radiation sensitive patient plus 2 or 3 controls selected from the radiation controls, skin cancer patients, and non-cancer controls. We withheld one subset and trained NSC on the remaining samples to identify a set of predictive genes, which defined a radiation sensitive centroid and a control centroid. Each sample from the withheld subset was classified by its proximity to the nearest centroid. This protocol was repeated for each of the 14 subsets until every sample was classified. To avoid biasing our predictions, samples from the 9 subjects analyzed by SAM were excluded as training samples for NSC, but were assigned probabilities for radiation toxicity.

[0090] Hierarchical clustering (Eisen et al. (1998) Proc. Natl. Acad. Sci. USA; 95:14863-14868) used centered Pearson correlation and complete linkage clustering, and was displayed with TreeView. Biological functions were assigned from the literature and the SOURCE database.

[0091] Results

[0092] Radiation sensitive patients and controls. Fourteen radiation therapy patients were enrolled after suffering unusual levels of radiation toxicity within one month of treatment, as judged by a faculty member in the Department of Radiation Oncology at Stanford. Toxicity was severe enough so that 11 of these 14 patients required interruption or early termination of treatment. These interventions helped limit the reported toxicities to grades 2 and 3. Thirteen patients with radiation toxicity limited to grades 0 or 1 were recruited as controls. We attempted to match this patient group to the radiation sensitive group by radiation field and dose, tumor type, gender, and concurrent chemotherapy (Table 4). The average age of the radiation control patients was 59 years ±13 years, while the average age of the radiation sensitive patients was 51 years ±11 years. Since the risk of radiation toxicity increases with age (Turesson et al. (1996) Int J Radiat Oncol Biol Phys; 36:1065-75), the younger age of the radiation sensitive patients was protective and should enhance the validity of our results. This study incorporated significant heterogeneity in radiation treatments. Importantly, the radiation sensitive group was matched to the radiation control group. This facilitated our goal to find genes that predicted acute toxicity, independently of the underlying tumor or site of treatment. TABLE 4 Clinical characteristics of radiation therapy patients Age/gender/diagnosis Patient Reaction Grade Radiation/concurrent chemotherapy Radiation sensitive patients 37F breast cancer RadS4* skin 3‡ 45 Gy to breast 49F breast cancer RadS14 skin 2‡ 50 Gy to breast, 10 Gy boost/cytoxan, 5- FU 53F breast cancer RadS12 skin 2‡ 55 Gy to breast 65F breast cancer RadS1 *skin 3‡ 45 Gy to breast 37F Hodgkin's disease RadS10 skin; breast 3‡ 40 Gy mantle field, 10 Gy neck boost cancer 20y later 50M Hodgkin's disease RadS6 skin; stroke 3 44 Gy mantle field 8y later 67M Hodgkin's disease RadS8 pneumonitis 2‡ 43 Gy mantle field, 36 Gy spade field 57M low grade lymphoma RadS7 mucositis; 3‡ 50 Gy to mandible & neck, 45 Gy to hip, hip osteonecrosis & jaw, cystitis 10y later of 60M low grade lymphoma RadS2 *skin 3† 31 Gy to lacrimal glands in both orbits 41M cancer of tongue RadS3 *mucositis 3‡ 70 Gy to tongue/tpz, cisplatin, 5-FU 45M salivary gland cancer RadS9 skin, 3‡ 40 Gy to oral cavity, 48 Gy to neck, 12 Gy mucositis to tongue/cisplatin, 5-FU 67F endometrial cancer RadS13 diarrhea 3† 42 Gy to pelvis 52F orbital pseudotumor RadS11 orbital edema 2 31 Gy to orbit 33F brainstem AVM RadS5 *cerebral 3 18 Gy stereotactic radiation to brainstem edema Radiation control patients 45F breast cancer RadC8 skin 1 50 Gy to chest wall 59F breast cancer RadC7 skin 1 50 Gy to breast, 10 Gy boost 65F breast cancer RadC9 skin 1 50 Gy to breast, 10 Gy boost 73F breast cancer RadC12 skin 1 50 Gy to breast 78F breast cancer RadC13 skin 1 50 Gy to breast, 10 Gy boost 39F Hodgkin's disease RadC1 none 0 44 Gy total lymphoid irradiation 49F Hodgkin's disease RadC4 none 0 44 Gy mantle field 46M mixed cell lymphoma RadC2 none 0 36 Gy to para-aortic & inguinal nodes, 31 Gy to orbital recurrence 63M large cell lymphoma RadC3 none 0 36 Gy to parotid gland 50F salivary gland cancer RadC5 skin 1 56 Gy to oropharynx 56M cancer of tonsil RadC10 skin, 1 70 Gy to oropharynx/cisplatin, 5-FU mouth dryness 70F cancer of oropharynx RadC6 skin, 1 66 Gy to oropharynx mouth dryness 76M cancer of tongue RadC11 skin, 1 70 Gy to oropharynx/tpz, cisplatin, 5-FU mouth dryness

[0093] Cells were exposed to UV as well as IR to determine whether some radiation sensitive patients have a general defect in responding to DNA damage. Because skin cancer is associated with UV exposure, we enrolled 15 patients diagnosed with skin cancer before age 40 to serve as additional controls. A successful classification method should not assign a high risk for radiation toxicity to the skin cancer patients. Fifteen subjects without cancer were matched to the skin cancer patients for age, gender, and race. Because we recruited patients with early skin cancer, their average age was 38 years ±8 years, and the average age of the normal individuals was 31 years ±5 years, which were significantly younger than the age of the radiation sensitive patients. A total of 57 subjects were recruited for study.

[0094] Analysis by SAM and nearest shrunken centroids. To identify genes normally induced or repressed by IR or UV, we applied SAM to data from 9 subjects without a history of cancer. SAM identified 1491 IR-responsive genes and 2114 UV-responsive genes. We previously developed an enhancement of nearest centroids, nearest shrunken centroids (NSC), which successfully identified small sets of highly predictive genes for other classification problems. However, when we applied NSC to these IR and UV-responsive genes, classification required 1831 genes while generating 10 errors.

[0095] Heterogeneity-associated transformation. A new approach was needed to identify predictive genes. Radiation toxicity can arise from several different underlying genetic defects, generating divergent transcriptional responses. For example, one subset of radiation sensitive patients could have a defect in signaling through ATM, leading to a failure to activate p53 after IR and a blunted response in p53-induced genes. Another subset could have a defect in DNA repair, leading to prolonged activation of ATM and enhanced transcription of p53-induced genes.

[0096] To address the problem of heterogeneity, we performed the following heterogeneity-associated transformation (HAT) $\begin{matrix} {{x^{\prime}(i)} = \left\lbrack {{x(i)} - {{\overset{\_}{x}}_{c}(i)}} \right\rbrack^{2}} & {{Equation}\quad 1} \end{matrix}$

[0097] where x(i) is the change in expression for gene i, and {overscore (x)}_(c)(i) is the average change in expression for gene i among the control samples. HAT generates similar values from changes in gene expression that are blunted in some cases or enhanced in others, and hence can capture heterogeneous abnormalities among the radiation sensitive patients. Simulations of microarray data demonstrated that NSC/HAT is more efficient than NSC alone in identifying genes with heterogeneous responses, but less efficient in identifying genes with homogeneous responses.

[0098] Genes with heterogeneous transcriptional responses were successfully identified after transforming the data with HAT. FIG. 1 shows the effect of HAT on two predictive genes, cyclin B and 8-oxo-dGTPase. When x′(i) replaced x(i) for the set of 1491 IR-responsive genes and 2114 UV-responsive genes, NSC identified a subset of 24 genes that predicted radiation toxicity, with 5 false negatives and no false positives (FIG. 2). The low error rate occurred for a wide range of threshold values for the nearest shrunken centroid classifier. Thus, HAT enhanced the power of NSC, suggesting that the radiation sensitive patients constitute a heterogeneous group.

[0099] Prediction of radiation toxicity. Of the 24 predictive genes, 20 were IR-responsive, and 4 were UV-responsive. NSC/HAT used these responses to compute a probability of radiation toxicity for each subject in the 48-sample training set (FIG. 3, upper panel). The separation between the radiation sensitive patients and controls indicated a strong correlation between the responses of the 24 genes and radiation toxicity. This correlation was confirmed by 14-fold cross-validation, which predicted radiation toxicity in 9 of 14 patients, with no false positives among 43 controls, which included the 9 subjects previously used to identify the damage response genes, p=2.2×10⁻7 by Fisher's two-tailed exact test (FIG. 3, lower panel).

[0100] The genes identified during cross-validation were essentially the same as the genes identified from the full 48-sample training set. Among the 24 genes identified for each of the 14 cross-validation trials, 80% were among the 24 top-ranked genes from the 48-sample training set, and 99% were among the 52 top-ranked genes from that set (FIG. 4). To test the stability of the cross-validation protocol, we performed 10 new trials of 14-fold cross-validation by withholding different subsets of patients. All 10 trials successfully predicted toxicity in the same 9 of 14 patients with no false positives among the controls.

[0101] Delayed toxicity in the form of progressive damage after completion of treatment is a grave problem. Three patients (RadS6, RadS7, and RadS10) suffered grade 4 delayed toxicity, and all were predicted successfully (Table 4). Toxicity from non-genetic factors cannot be predicted by our approach. Of the 5 patients with radiation toxicity not predicted by NSC/HAT, at least 2 (RadS3 and RadS5) were at high risk for toxicity from non-genetic factors. Patient RadS3 suffered grade 3 mucositis from an experimental protocol that included high dose radiation plus tirapazamine, cisplatin, and 5-FU. Subsequent review of patients treated by this protocol revealed that 28 of 62 (45%) suffered mucositis of grade 3 or higher. Patient RadS5 had an arteriovenous malformation that was treated with stereotactic guidance of a single 18 Gy dose to a 1.8 cm³ volume in the midbrain and pons. A statistical model indicates that the midbrain and pons region has the highest probability for permanent symptomatic injury, with a 40% to 45% probability for the dose and volume delivered to RadS5. To determine whether RadS3 and RadS5 had an effect on the results, we excluded them and repeated the analysis. Despite the decreased number of samples available for training, NSC/HAT successfully predicted toxicity in 9 of the remaining 12 cases, with no false positives among 43 controls.

[0102] Ruling out confounding variables. The enormous number of genes analyzed by microarrays offers great opportunity for discovery. However, transcriptional responses that appear to be predictive might instead be due to a confounding variable. Here, the confounding variable could be some other difference between the radiation sensitive patients and the control subjects. The subjects with no cancer or skin cancer were younger than the subjects with radiation toxicity. They were also free of cancers of the internal organs, which might be associated with an abnormal response to DNA damage. Furthermore, they were never treated with IR, and 5% to 10% might be at risk for toxicity. To address this problem, we omitted the 30 subjects with no cancer or skin cancer and analyzed the 27 radiation therapy patients. T his restricted analysis was also successful despite the fewer samples available for training. A set of 13 genes yielded the same 5 false negatives reported above, with no false positives among the 13 controls. When tested on the 30 omitted subjects, these 13 genes predicted only 3 positives, consistent with the expected low risk for toxicity in the general population. The set of predictive genes was stable in the face of restricted analysis. Nine of the 13 genes were among the 24 top-ranked genes identified with the 48-sample training set, and 20 of the 24 predictive genes from the 48-sample training set were among the top 81 ranked genes in the restricted analysis.

[0103] Heterogeneity among the radiation sensitive patients. The 57 subjects and 52 top-ranked predictive genes identified by HAT/NSC were organized by hierarchical clustering (FIG. 4). The 52 genes were obtained from the 48-sample training set and included 40 IR-responsive genes and 12 UV-responsive genes. The radiation sensitive patients did not form a single cluster, suggesting that radiation toxicity arises from more than one type of underlying defect. Four radiation sensitive patients clustered loosely on the left side of the heat map. Cells from these patients had abnormal responses in many of the 52 genes, including the cluster of 9 UV-responsive genes at the bottom of the heat map. These patients may have a general defect in responding to DNA damage. Five radiation sensitive patients clustered on the right side of the heat map. These patients had a relatively normal response in the UV-response gene cluster, but had prominent defects in IR-response genes.

[0104] Genes with transcriptional responses that predict radiation toxicity. No single gene predicted radiation toxicity. Instead, the response of several genes provided a signature for toxicity. The 52 top-ranked predictive genes are involved in several different cellular processes (FIG. 4).

[0105] Four genes had roles in DNA repair. XPC-complementing protein (RAD23 homolog B) is involved in nucleotide excision repair. Its response to IR was abnormal in many radiation sensitive samples. The 8-oxo-dGTPase gene product (NUDT1) hydrolyzes 8-oxo-dGTP to 8-oxo-dGMP, which is then converted to the nucleoside, 8-oxo-dG, thus preventing misincorporation of 8-oxo-dGTP into DNA. Urinary 8-oxo-dG is a biomarker for oxidative DNA damage, and decreased levels correlated with acute radiosensitivity in breast cancer patients. These results may be explained by the abnormal IR-suppressed expression of 8-oxo-dGTPase we observed in several radiation sensitive patients (FIG. 1). IR-induced DNA double-strand breaks are repaired by homologous recombination (HR) or nonhomologous end-joining.

[0106] Human RuvB-like protein 1 (RUVBL1) is homologous to bacterial RuvB, a DNA helicase that catalyses branch migration of Holliday junctions during HR. RuvB-like proteins are also components of the yeast INO80 complex, which remodels chromatin, and confers resistance to DNA damaging agents. PTB-associated splicing factor (PSF) may be involved in HR by promoting DNA strand invasion. Interestingly, RUVBL1 and PSF responded abnormally to UV but not IR in many radiation sensitive patients. None of the 52 top-ranked predictive genes was involved in nonhomologous end-joining. However, this pathway does not respond to IR transcriptionally, but rather involves activation of a DNA-dependent protein kinase.

[0107] Five predictive genes are involved in the general stress response. Cells from radiation sensitive patients showed abnormal IR responses in genes encoding c-fos, MAP kinase-activated protein kinase 2 (MAPKAP2), heat shock protein 27 (HSPB1), which is a substrate of MAPKAP2 phosphorylation, and protein phosphatase 1A (PPM1A), which inhibits stress-activated protein kinase cascades. Abnormal UV responses were observed for calmodulin (CALM1).

[0108] Four predictive genes are involved in the ubiquitin/proteasome protein degradation pathway, which is induced by oxidative stress. Abnormal IR responses were observed for ubiquitin B (UBB), proteasome activator subunit (PSME2), and two subunits of the 26S proteasome, β subunit 4 (PSMB4) and the non-ATPase subunit 1 (PSMD1).

[0109] Three cell cycle genes responded abnormally to UV in some radiation sensitive patients: cyclin B1 (CCNB1), cyclin A2 (CCNA2), and CDC28 protein kinase 2 (CKS2), which negatively regulates CDK-cyclin complexes.

[0110] Apoptosis genes included tumor necrosis factor (TNFSF7), core binding factor (CBFB), and the mitochondrial adenine nucleotide transporter (ANT). ANT regulates mitochondrial membrane permeability during apoptosis. The fibroblast isoform of ANT (SLC25A6) responded abnormally to IR, and the liver isoform (SLC25A5) responded abnormally to UV in most radiation sensitive patients. Four predictive genes were involved in RNA processing, and the remaining 18 predictive genes were involved in a diverse set of pathways.

[0111] Many cases of radiation toxicity are associated with abnormal transcriptional responses to DNA damage. To identify a subset of highly predictive genes, we subjected the transcriptional responses to a heterogeneity-associated transformation (HAT). Classification by nearest shrunken centroids (NSC) with HAT predicted 9 of 14 cases of radiation toxicity with no false positives among 43 controls. Notably, the false positive rate was very low with a 95% confidence interval of 0% to 7%. Toxicity was successfully predicted in 64% of the radiation sensitive patients with a 95% confidence interval of 42% to 87% by the exact binomial distribution. Even the lower limit of this confidence interval suggests that a significant number of adverse radiation reactions are associated with abnormal transcriptional responses. Furthermore, 2 of the 5 patients not predicted by NSC/HAT were at high risk for radiation toxicity from non-genetic factors and may have been properly classified in terms of transcriptional responses.

[0112] These results are valid for several reasons. First, to guard against the identification of genes that later fail when tested on an independent set of samples, our results were subjected to cross-validation. We used 14-fold cross-validation, which is more robust than the commonly used “leave-one-out” approach. Second, we imposed the additional test of restricted analysis to rule out confounding variables; when we restricted the training set to the 27 radiation therapy patients, there was little effect on prediction error or on the identity of predictive genes. Third, we applied nearest centroids with HAT to the IR responses of all 12,625 probe sets on the microarray. On cross-validation, we successfully predicted 8 of 14 cases of radiation toxicity (RadS5, RadS7, and RadS9-14) with only 2 false positives (RadC8 and RadC9) among the 43 controls. Thus, our results were not an artifact of gene selection bias.

[0113] Finally, our protocol for predicting radiation toxicity used a plausible biological endpoint, the transcriptional response to DNA damage. Appropriately, 20 of the 24 top-ranked genes contributed IR responses, and only 4 genes contributed UV responses. When we attempted to predict radiation toxicity from the less plausible endpoint of basal gene expression, we obtained a low error rate after cross-validation. However, basal expression failed our additional test of restricting analysis to the radiation therapy patients; the prediction error rate increased significantly, and the set of predictive genes changed markedly, indicating the presence of confounding variables that affected basal gene expression.

[0114] The mechanisms of radiation toxicity are heterogeneous. Some radiation sensitive patients had abnormal transcriptional responses to both UV and IR, and others had abnormal responses only to IR. The abnormal responses involved genes from a diverse set of pathways with functions in DNA repair, response to stress, protein degradation, cell cycle regulation, apoptosis, and RNA processing. The genes with abnormal responses may not be mutated, but rather reflect an abnormality in some other gene. For example, abnormal responses in both UV and IR could arise from mutations affecting p53 or ATR. In patients with abnormal responses restricted primarily to IR, the underlying mutations could be in the ATM-dependent signaling pathway or a DNA double-strand break repair pathway. Radiation toxicity may also arise from the combined effect of polymorphisms in several genes.

[0115] It is evident that subject invention provides a convenient and effective way of determining whether a patient will be responsive to therapy. The subject methods will provide a number of benefits, including avoidance of delays in alternative treatments, elimination of exposure to adverse effects of therapeutic antibodies and reduction of unnecessary expense. As such, the subject invention represents a significant contribution to the art.

[0116] All publications and patent applications cited in this specification are herein incorporated by reference as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention.

[0117] Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, it is readily apparent to those of ordinary skill in the art in light of the teachings of this invention that certain changes and modifications may be made thereto without departing from the spirit or scope of the appended claims. 

What is claimed is:
 1. A method of predicting whether a subject will be susceptible to undesirable toxicity resulting from treatment with an anti-proliferative therapy, said method comprising: (a) obtaining an expression profile for any gene from Table3 for a response to said anti-proliferative therapy in a sample from said subject; and (b) comparing said obtained expression profile to a reference expression profile to predict whether said subject is susceptible to undesirable toxicity.
 2. The method according to claim 1, wherein said anti-proliferative therapy comprises administration of ionizing radiation.
 3. The method according to claim 1, wherein said anti-proliferative therapy comprises administration of a chemotherapeutic agent that results in DNA damage.
 4. The method according to claim 3, wherein said DNA damage comprises double-stranded breaks in DNA.
 5. A method of determining whether a subject is susceptible to undesirable toxicity resulting from treatment with radiation therapy, said method comprising: (a) obtaining an expression profile for the response to radiation for a sample for any gene from Table3 from said subject; and (b) comparing said obtained expression profile to a reference expression profile to determine whether said subject is susceptible to undesirable radiation toxicity.
 6. The method according to claim 5, wherein expression profile is a transcriptional profile.
 7. The method according to claim 5, wherein said expression profile comprises at least 10 sequences from Table3.
 8. The method according to claim 5, wherein said expression profile comprises at least 50 sequences from Table
 3. 9. The method according to claim 5, wherein said undesirable toxicity is at least a grade 2 toxicity.
 10. A method of determining whether a subject is susceptible to undesirable toxicity resulting from treatment with administration of a chemotherapeutic agent that induces double-stranded breaks in DNA, said method comprising: (a) obtaining an expression profile for the response to said chemotherapeutic agent for a sample for any gene from Table3 from said subject; and (b) comparing said obtained expression profile to a reference expression profile to determine whether said subject is susceptible to undesirable toxicity.
 11. A method of predicting whether a subject will be susceptible to undesirable toxicity resulting from treatment with radiation therapy, said method comprising: (a) obtaining an expression profile for the response to radiation for a sample for any gene from Table3 from said subject; and (b) comparing said obtained expression profile to a reference expression profile to determine the probability that said subject is susceptible to undesirable radiation toxicity.
 12. The method according to claim 11, wherein expression profile is a transcriptional profile.
 13. The method according to claim 11, wherein said expression profile comprises at least 10 sequences from Table
 3. 14. The method according to claim 11, wherein said expression profile comprises at least 50 sequences from Table
 3. 15. The method according to claim 11, wherein said undesirable toxicity is at least a grade 2 toxicity.
 16. A method of predicting whether a subject will be susceptible to undesirable toxicity resulting from treatment with administration of a chemotherapeutic agent that induces double-stranded breaks in DNA, said method comprising: (a) obtaining an expression profile for the response to said chemotherapeutic agent for a sample for any gene from Table3 from said subject; and (b) comparing said obtained expression profile to a reference expression profile to determine the probability that said subject is susceptible to undesirable toxicity.
 17. A method of determining the suitability of a patient for radiation therapy, the method comprising: predicting whether a subject will be susceptible to undesirable toxicity resulting from treatment with radiation therapy, said method comprising: (a) obtaining an expression profile for the response to radiation for a sample for any gene from Table3 from said subject; and (b) comparing said obtained expression profile to a reference expression profile to determine the probability that said patient is susceptible to undesirable radiation toxicity; wherein a patient that is predicted to have a high probability of undesirable radiation toxicity is less suitable for radiation therapy.
 18. The method according to claim 17, wherein expression profile is a transcriptional profile.
 19. The method according to claim 17, wherein said expression profile comprises at least 10 sequences from Table
 3. 20. The method according to claim 17, wherein said expression profile comprises at least 50 sequences from Table
 3. 21. The method according to claim 17, wherein said undesirable toxicity is at least a grade 2 toxicity.
 22. A method of determining the suitability of a patient for treatment with an anti-proliferative chemotherapeutic agent that induces double-stranded breaks in DNA, the method comprising: predicting whether a subject will be susceptible to undesirable toxicity resulting from treatment with said chemotherapeutic agent, said method comprising: (a) obtaining an expression profile for the response to said chemotherapeutic agent for a sample for any gene from Table3 from said subject; and (b) comparing said obtained expression profile to a reference expression profile to determine the probability that said patient is susceptible to undesirable toxicity; wherein a patient that is predicted to have a high probability of undesirable toxicity is less suitable for said treatment with an anti-proliferative chemotherapeutic agent.
 23. A method of optimizing anti-proliferative therapy for a patient, the method comprising: (a) obtaining an expression profile for the response to said anti-proliferative therapy for a sample for any gene from Table3 from said subject; and (b) comparing said obtained expression profile to a reference expression profile to determine the probability that said patient is susceptible to undesirable toxicity; wherein a dose of said anti-proliferative therapy is selected to minimize to undesirable toxicity, while providing for effective anti-proliferative activity.
 24. The method according to claim 23, further comprising obtaining an expression profile for a response to one or more additional anti-proliferative therapies; comparing said expression profiles to determine which therapy minimizes undesirable toxicity while providing for effective anti-proliferative activity.
 25. The method according to claim 23, further comprising obtaining an expression profile for the response to said anti-proliferative therapy for (i) a normal cell sample for any gene from Table3 from said subject and (ii) a tumor cell sample for any gene from Table3 from said subject; comparing said expression profiles from said normal cell and said tumor cell to determine which therapy minimizes undesirable toxicity while providing for effective anti-proliferative activity.
 26. A method of obtaining an expression profile for the transcriptional response to radiation, the method comprising: exposing a cell sample from an individual to radiation; extracting mRNA from said cell; quantitating the level of mRNA corresponding to a sequence in Table 3; comparing said level of mRNA to the level of said mRNA present in a cell sample from said individual not exposed to radiation.
 27. The method according to claim 26, wherein said exposing to radiation comprises exposes said cell to a dose of ionizing radiation of from about 2 to about 10 Gy.
 28. The method according to claim 27, wherein said mRNA is extracted after at least about 2 and not more than about 24 hours following said exposure.
 29. The method according to claim 27, further comprising exposing a cell sample from said individual to ultraviolet radiation at a dose of at least about 5 J/m² and not more than about 50 J/m².
 30. The method according to claim 29, wherein said mRNA is extracted after at least about 4 and not more than about 72 hours following said exposure.
 31. The method according to claim 26, wherein said comparing step comprises a nearest shrunken centroid analysis step.
 32. A method of obtaining an expression profile for the transcriptional response in a phenotype of interest, the method comprising: exposing a cell sample from an individual to said anti-proliferative therapy; extracting mRNA from said cell; quantitating the level of mRNA corresponding to a sequence of interest; comparing by nearest shrunken centroid analysis said level of mRNA to the level of said mRNA present in a cell sample from said individual not exposed to said anti-proliferative therapy.
 33. The method according to claim 32, wherein said phenotype of interest comprises anti-proliferative therapy.
 34. A kit for determining susceptibility to undesirable toxicity, the kit comprising: a set of primers specific for at least 10 genes as set forth in Table 3; and instructions for use.
 35. The kit according to claim 34, further comprising a set of primers specific for at least 25 sequences set forth in Table
 3. 36. The kit according to claim 34, further comprising a set of primers specific for at least 50 sequences set forth in Table
 3. 37. The kit according to claim 34, further comprising a software package for statistical analysis of expression profiles.
 38. A kit for determining susceptibility to undesirable toxicity, the kit comprising: a microarray comprising probes specific for at least 10 genes as set forth in Table 3; and instructions for use.
 39. The kit according to claim 38, further comprising probes specific for at least 25 sequences set forth in Table
 3. 40. The kit according to claim 38, further comprising probes specific for at least 50 sequences set forth in Table
 3. 41. The kit according to claim 38, further comprising a software package for statistical analysis of expression profiles.
 42. A method for determining a set of sequences whose expression is predictive of a phenotype of interest, the method comprising: obtaining an expression profile for a set of candidate sequences from a cohort group having said phenotype of interest and from a control group; subjecting said expression profiles to heterogeneity associated transformation; analyzing transformed or untransformed expression profile data by nearest shrunken centroids; and calculating the probability that a specific sequence is predictive of said phenotype of interest.
 43. The method according to claim 42, wherein said sequence is a nucleotide sequence expressed in a target cell.
 44. The method according to claim 42, wherein said sequence is a protein sequence expressed in a target cell.
 45. The method according to claim 42, wherein said sequence is a protein sequence post-translationally modified in a target cell.
 46. The method according to claim 42, wherein said obtaining an expression profile for the transcriptional response to radiation comprises: exposing a cell sample from an individual to stimulus; extracting mRNA or protein from said cell; quantitating the level of mRNA or protein; comparing said level of mRNA or protein to the level present in a cell sample from said individual not exposed to said stimulus. 